text:
\section{Introduction}
Adversarial learning ~\cite{Tygar11} is an instance of machine learning where two entities called the learner and adversary attempt to learn a prediction mechanism for data related to a problem domain at hand, albeit with different objectives. The learner's objective in learning the prediction mechanism is to correctly predict or classify the data. In contrast, the adversary's objective is to imperceptibly coerce the learner into making incorrect predictions for the data in the future. A very popular instance of adversarial learning is email spam filtering~\cite{Tygar11,Huang11}. Here, the learner is the spam filter with a prediction mechanism that classifies incoming email into two categories, spam or non-spam. On the other hand, the adversary is the spammer who in addition to generating spam email, also tries to add, remove or alter certain words or characters in the email text~\cite{Dalvi04} so that it can disguise non-spam email as spam, and vice-versa. If the spammer is succcessful, the spam filter ends up mis-classifying the altered non-spam emails as spam (false positives) or the altered spam emails as non-spam (false negatives). Both these mis-classifications could be dangerous for the integrity of the email filtering system, not only do they block legitimate email and allow potentially malicious spam email to pass through, but they also reduce the confidence in the email classification performed by the spam filter. Adversarial learning poses a severe cybersecurity threat in several domains that employ machine learning-based classifier systems including automated email spam filters and anti-virus software, image classification algorithms in defense and medical applications, and text-based sentiment analysis algorithms used on social media data. To combat these challenges, researchers have proposed several techniques that aim to make the learner's classifier robust against adversarial attacks~\cite{Huang11}. Many of these techniques employ a popular framework of decision making at the intersection of mathematics, economics and computer science called game theory~\cite{Fudenberg91,Myerson97,LeytonBrown09}. Game theory provides an attractive tool for adversarial learning as it provides a means to mathematically model the learner and adversary's behaviors in terms of defense and attack strategies and determine suitable strategies to reduce the learner's loss from adversarial attacks. In this survey, we focus on such game theory-based techniques that have been used to make machine learning algorithms robust against adversarial attacks.

\begin{figure*}[thb!]
\begin{center}
\includegraphics[width=2.0in]{attack-taxonomy2.eps}
\caption{{\small{Taxonomy of different types of adversarial attacks along three dimensions proposed in~\cite{Huang11}. Influence and security violation-based attacks are divided into distinct categories marked by solid and dashed dividing lines. Specificity attacks range over a continuous spectrum marked as dotted arrows.}}}
\label{fig_adv_taxonomy}
\end{center}
\end{figure*}

The rest of the paper is structured as follows: in the next section we provide background information on adversarial learning and game theory. Following that, we summarize the contributions of game theory-based adversarial learning approaches and solution techniques. Then, we discuss open issues and challenges for future research directions in game theory-based modeling of adversarial learning, and, finally we conclude. Also, in the rest of the paper, in accordance with machine learning literature, we assume that the output of the learner's prediction mechanism classifies the data into a finite set of classes, each class is identified with an output label. Finally, for legibility, while following most of the existing literature in this area, we assume that the learner uses a classifier for its prediction mechanism. In general, the learner could use any other prediction mechanism like clustering, ranking or regression.

\section{Background: Adversarial Learning and Games}
\label{sec_background}
{\em Adversarial Learning.} Adversarial learning deals with techniques used by a machine learning-based prediction mechanism such as a classifier to make itself robust against adversarial attacks. In~\cite{Huang11}, authors provide a comprehensive overview of adversarial learning techniques. Their work includes a taxonomy for adversarial learning while characterizing it along three dimensions - influence, specificity and security violation, as shown in Fig.~\ref{fig_adv_taxonomy}. Influence is the most relevant and widely researched dimension for adversarial learning because it characterizes the adversary based on its behavior, with the objective of developing appropriate learner strategies to counter the adversary's behavior. The influence dimension specifies two types of adversarial attacks called {\em causative} and {\em exploratory} (a.k.a. probing) that are illustrated in Fig.~\ref{fig_adv_attacks}. In {\em causative attacks} the adversary acquires data that was used to train the learner's classifier and modifies this data. This modified data, called adversarial data, is then used by the learner during further training of its classifier. This results in the learner learning an incorrect classifier that gives classification errors (false positives and false negatives) while testing or while using the classifier. In {\em exploratory attacks}, the adversary observes the output of the learner's classifier for different data and tries to discover its vulnerabilities (e.g., what data it mis-classifies). It then creates adversarial data that exploits those vulnerabilities to increase the classifier's mis-classification rate during test or application time. Two other dimensions of adversarial learning shown in Fig.~\ref{fig_adv_taxonomy} are security violation and specificity. The reader is referred to~\cite{Huang11}, for approaches that counter adversarial attacks along these three dimensions. The rest of this paper focuses specifically on how the influence dimension's causative and exploratory attacks are countered with game theory-based techniques.

\begin{figure*}[thb!]
\begin{center}
\includegraphics[width=4.8in]{adv-attacks.eps}
\caption{{\small{Adversarial learning scenario showing the two types of influence-based adversarial attacks - causative (top right) and exploratory (bottom right). The learner uses a support vector machine to classify input. Red color input represents adversarial examples that are created by the adversary using a perturbation function $\phi()$ from valid examples from the training set and re-injected into the training set.}}}\textsl{•}
\label{fig_adv_attacks}
\end{center}
\end{figure*}

{\em Non-cooperative Game.} Adversarial learning has been extensively modeled as a $2$-player, non-cooperative game. A non-cooperative game can be informally defined as an interaction between two or more players over a resource that has to be shared between the players. The game, represented in normal form, is given by $(N, A, U)$, where $N$ is the set of players, $A=\{A_i\}$ where $A_i$ is the set of actions for player $i$, and, $U=\{U_i\}$, were $U_i(a_i, a_{-i})$ gives a real-valued number called utility received by each player $i \in N$ when it selects action $a_i \in A_i$ while other players jointly select $a_{-i} \in A_{-i}, A_{-i} = \times A_{j \neq i}$. The utility of each player gives its preference over the different outcomes of the game resulting from different joint actions by the players. Player $i$'s strategy set $S_i=\{\pi(A_i): \pi(A_i)\geq 0\;\forall i, \sum_{A_i} \pi(A_i)=1\}$ specifies a probability distribution over its actions $A_i$. The outcome of a game is a strategy selected by each player. One of the most widely used techniques to calculate a player's strategy in a game is given by Nash equilibrium (NE). NE assumes that players behave rationally and each player $i$ plays its {\em best response} strategy given by $s_i$ that satisfies \(U_i(s_i, s_{-i}) \geq U_i(s'_i, s_{-i}), \forall i \in N\). The NE of a $2$-player game can be represented as a linear complimentarity problem and solved using linear programming or as a search problem~\cite{LeytonBrown09}. Below, we briefly mention a few aspects of games that are relevant to adversarial learning. Because adversarial learning is a $2$-player game between the learner and the adversary, we denote the two players using subscripts $l$ and $a$ respectively.

{\em Zero-sum vs. Non-zero Sum Game}. In a $2$-player, zero-sum game, the utilities of players, the learner and adversary, sum to zero. In other words, the gain in utility of the learner comes at the cost of loss of adversary's utilities and vice-versa. In a $2$-player, zero-sum game, the NE can be calulated using the minimax theorem, which says that the game's Nash equilibrium outcome is the same as its minimax outcome. The minmax outcome can be represented as constrained optimization problem and solved as a linear program. Adversarial learning has been extensively modeled as a $2$-player, zero sum game. However, recently Bruckner {\em et al.}~\cite{Bruckner11} observe that assuming adversarial learning is a zero-sum game is overly pessimistic - the utility loss of the learner might not equal the utility gain of the adversary. Consequently, they model adversarial learning as a non-zero sum or general sum game. Unfortunately, minmax theorem's result does not hold for general sum games, and, more complicated, general Nash equilibrium solution techniques~\cite{LeytonBrown09} have to be used to determine the learner and adversary's selected strategies.

{\em Simultaneous Move vs. Sequential Game.} In a simultaneous move game, players make their strategy selection simultaneously and cannot observe each others' strategy before selecting their own. In contrast, in a sequential move game, players take turns in selecting their strategies (or, making their moves). Adversarial learning has been modeled as the latter - the learner is the player making the first move or the leader, because it usually publishes its classifier and is not aware of the presence of the adversary~\cite{Huang11,Groshans13}. The adversary, on the other hand, is the follower because it can observe the learner's classifier and then make its move of selecting a suitable strategy to generate adversarial instances. Sequential move games are easier to solve than simultaneous move games because the follower knows the leader's selected strategy and can use this information to select its utility-maximizing strategy. The leader's strategy selection technique, however, does not have information about the follower's selected strategy. Consequently, while selecting its strategy, the leader has to incorporate this uncertainty about the follower's strategy using a type of game called a Bayesian game, as described next.

{\em Bayesian Game}. The normal form game mentioned above assumes that each player has information about the utilities of other players, for each action. This assumption might not be valid in many practical, real-life scenarios because it is unrealistic for a player to have accurate information about competing player's utilities. For example, in adversarial learning, the learner might not have accurate information of the adversary's cost to generate adversarial data~\cite{Bruckner11,Groshans13} or the adversary might not have accurate information about the learner's classification cost~\cite{Lowd05}. The problem is addressed through a Bayesian game~\cite{Harsanyi68}, where each player is assumed to have a set of types. The utility that a player gets from each of its actions now also depends on its types. A player does not know the exact type of other players, but it knows the probability distribution over the types. Based on this information, a player can calculate expected utilities, conditioned on the other players' types. Some researchers have modeled adversarial learning as a Bayesian sequential-move game, where the learner assumes a set of types for the adversary along with probability distribution over the types. It then selects a strategy based on its expected utilities conditioned on the prior probabilities of the different adversary types, as discussed in the section on non-zero sum games, later on in our article.

It is worth mentioning that the topic of security games is closely related to adversarial learning, althought the roles and objectives of the learner and adversary in a security game are slightly different from those in adversarial learning. In security games~\cite{Paruchuri08}, the learner is called the defender, whose  objective is to protect a set of targets from an adversary, referred to as an attacker. The problem facing the defender is to allocate protection resources within budget and operational constraints, to achieve a desired level of security. Security games have been applied to different real-life applications including airport security~\cite{Pita11}, wildlife protection~\cite{Fang17} and natural resource conservation~\cite{Ford16}. An excellent discussion on security games is available in~\cite{Tambe11}.

\section{Game Theory based Adversarial Learning Techniques}
\label{sec_gt_adv_learning}
Adversarial learning is usually modeled as a $2$-player game between the learner and adversary. The learner's set of actions corresponds to selecting different hyper-parameters for its classifer, while the adversary's set of actions correspond to different strategies for changing valid data into adversarial data. For example, the adversary's actions could be to add different amounts of perturbation or noise to valid data, or, to remove certain features from valid data.
The utilities for the learner and the adversary are defined in terms of their joint actions.

\begin{table*}
\begin{center}
\hspace*{-0.3in}
\begin{tabular}{|c|c|c|c|}
\hline
{\bf References}&{\bf Initial Information about learner}&{\bf Adversary Attack Model}& {\bf Validation domain}\\
& {\bf with adversary} & & \\
\hline
\rowcolor{Gray}
\multicolumn{4}{|c|}{\bf Zero Sum Games}\\
\hline
\cite{Dalvi04} & Full information about learner's & Causative attacks & Spam filtering\\
			   & utility, cost and  classifier parameters & &\\
\hline
\cite{Lowd05,Nelson10,Li14,Vorobeychik14}& No information about learner's & Exploratory attacks by changing & Spam filtering\\
									& utility, costs and classifier parameters&values of future  input&\\
\hline
\cite{Globerson06,Dekel10,Teo07}& No information about learner's  & Exploratory attacks by removing & Spam filtering\\
									&  utility, costs and classifier parameters&  features from future input&\\
\hline
\cite{Hardt16}					& With and without info. about prob.& Exploratory attacks by changing & Spam filtering\\
								&  distribution of input and ground truth  & values of future  input &\\
\hline
\cite{Goodfellow14}				& Full information about learner's & Exploratory attacks counterfeiting  & Image classification\\
								& utility, cost and classifier params &valid input & \\
\hline
\rowcolor{Gray}
\multicolumn{4}{|c|}{\bf Sequential, Bayesian, Non-zero Sum Games}\\
\hline
\cite{Bruckner11,Groshans13,Bruckner12}& No information about learner's & Exploratory attacks by changing  & Spam filtering\\
\cite{Groshans15,Mei15,Bulo16}				& utility, costs and  classifier parameters		& values of future input&\\
\hline
\cite{Alfeld17}& No information about learner's	& Exploratory attacks on test& Stock prices \\
& 	utility, costs and  classifier parameters &  set only & \\
\hline
\cite{Zhou12, Zhou14,Dritsoula17}&  No information about learner's           &Exploratory attacks by mixing valid& Spam filtering\\
								& utility, costs and classifier parameters	&  and adversarial input, e.g., altering  &\cite{Zhou12,Zhou14} \\
								&                                            & all or part of input features &\\
\hline
\end{tabular}
\caption{Comparison of game theory-based adversarial learning techniques.}
\label{table_summary}
\end{center}
\end{table*}

In one of the earliest and seminal works on adversarial classification Dalvi {\em et al.}~\cite{Dalvi04} formulated adversarial classification as a $2$-player competitive game between the learner and the adversary called a classification game. The learner's prediction mechanism is a binary classifier while the adversary creates adversarial input by perturbing features from legitimate input. The game is asymmetric as the adversary is aware of the learner's classifier parameters, utilities and costs, but the learner is not aware whether an input is adversarial versus legitimate. Within this setting, the learner's utility is defined as its value from classifying input (misclassification yields negative value), minus a per-feature cost for including input features in the classification algorithm. Similarly, the adversary's utility is defined as its value from misclassification of an adversarial input by the learner (correct classification by learner yields negative value to adversary), minus its cost to generate the adversarial input from legitimate input. Both learner and adversary play a Nash equilibrium strategy. The problem is formulated as a constrained optimization problem and solved as a mixed integer linear program for the adversary that is then used by the learner to determine a robust classification strategy. The proposed strategy is validated using spam email data sets with different adversarial perturbation strategies and different learner misclassification penalties and shown to yield positive classifier utilties, implying low misclassfication rates of adversarial input by the learner.

Following ~\cite{Dalvi04}, researchers have proposed different approaches to define these utilities depending on their learner and adversary behavior models. We categorize the solution techniques proposed in literature into two categories - those that use zero-sum games to model the interaction between the learner and adversary followed by a minimax-based, linear optimization solution to solve it, and, those that model the interaction as a non-zero sum game and use a Nash equilibrium-based, bi-level optimization, or related solution. Table ~\ref{table_summary} gives a list of the major game theory-based approaches for adversarial learning along different learner and adversary models and solution techniques.

\subsection{Zero Sum Games: Constrained Optimization-based Solution Techniques}
\label{sec_gt_zero_sum_games}

Lowd and Meek~\cite{Lowd05}, extended Dalvi {\em et al.}'s model while relaxing the assumption that the adversary has full information about the learner's classification algorithm, utilities and costs. Although their main algorithm called Adversarial Classifier Reverse Engineering (ACRE) is not based on a game, we review it here as it has formed the basis for future game theory-based techniques for adversarial learning. In the adversarial learning problem considered by~\cite{Lowd05}, the adversary can discover information about the learner's classifier by sending a limited number of queries containing adversarial input. With this limited knowledge of the learner's classifier, the adversary's objective is to determine the input instance that incurs the lowest cost to modify into an adversarial input. The set of such input instances are called attack vectors. Using the ACRE algorithm, the adversary can find attack vectors that can defeat the learner's classification algorithm when the input consists of either continuous or binary features. The algorithm is validated on spam email data sets when the learner uses either a Naive Bayes or a maximum entropy based classifier and shows that an adversary using ACRE can find instances within $17\%$ of the actual lowest cost instance while using a few thousand queries. In~\cite{Nelson10}, authors generalized ACRE to an $\epsilon$-instance minimal adversarial cost problem, where the learner's set of classifiers is expanded from linear classifier to more general, convex-inducing classifiers. Their solution searches over the adversarial cost space to determine the minimum set of adversarial examples, called the evasion set, that needs to be generated by the adversary to effect classification errors by the learner. Unlike~\cite{Lowd05}, avoiding the reverse engineering of the classifier allows their approach to handle classifiers that are difficult to reverse engineer. Recently, Li and Vorobeychik ~\cite{Li14}~\cite{Vorobeychik14} also extended the ACRE framework. Their proposed techniques include defining the cost between instances using equivalence class-based cost functions and solving the optimization problem facing the adversary as a mixed integer linear program, and, using a concept called moving target defense~\cite{Jajodia12} where the learner employs randomization over multiple classifiers instead of tuning parameters of a single classifier to make its prediction robust against adversarial attacks.

Another parallel, but related line of research, considers the adversary's behavior from a slightly different approach, where the adversary generates adversarial data by selecting, removing or corrupting features from the input data set. Globerson {\em et al.}~\cite{Globerson06} describe such a setting where the adversary can remove multiple or single features from input and the learner's objective is to determine an optimal set of feature weights for its classifier, that minmize a metric called the hinge loss. The problem is formulated as a minimax, zero sum game and represented as a constrained optimization problem. The proposed algorithm was verified with adversarial data and shown to give lower error rates than a support vector machine classifier for handwritten digit classification and spam filtering. Globerson's model of selective feature removal by the adversary was extended in~\cite{Dekel10} with two variants of the learner. When the learner is able to train a classifier using training data, the problem is solved as a linear program. On the other hand, when the learner does not have access to training data, the learning task becomes an online learning problem. In this case, the learner's classifier is determined using a neural network, perceptron algorithm~\cite{Rosenblatt58} followed by a batching technique to model the online classifier as a statistical learning algorithm while making statistical guarantees about the classifier's performance. Experiments comparing the authors' algorithm with those in~\cite{Globerson06,Teo07} while using the same data sets, show that their proposed technique improves classification accuracy over the compared techniques.

In~\cite{Hardt16}, the authors consider adversarial learning games from the perspective of information revelation by the learner and adversary. They consider two variants of the learner: (i) when the learner has information about adversary's cost function, ground truth and input distribution, and (ii) when the learner knows only adversary's cost function, but not ground truth or input distribution. Similarly, two types of adversary are considered: one that knows all parameters that the learner knows plus its own adversarial example generation function, and, another, that knows only its own cost function and adversarial example generation function.  Within these settings, the learner's objective is to determine a {\em strategy robust algorithm} - one that selects a classifier  that maximizes the probability of the classifier's output corresponding to the ground truth for possibly adversarial examples. On the other hand, the adversary tries to create adversarial examples that maximize its utility  given by the difference between its benefit from the learner's classifier output for that example and its cost to generate the example. Their results theoretically analyze the running time and sample complexity of the learner for different types of adversary functions called separable and non-separable.

As mentioned earlier, most existing literature on adversarial learning games assume a sequential-move game with the learner as the leader. However, few researchers have analyzed adversarial learning where the learner knows the adversary's strategy to generate adversarial data, but the adversary does not have information about the learner's classifier. This makes the adversary the leader and the learner the follower~\cite{Kantarcioglu11,Liu09}. Liu and Chawla~\cite{Liu09} consider such a setting where the adversary tries to generate adversarial data that results in moving the learner's classification boundary between spam versus non-spam email. The learner uses a genetic algorithm to search classifier parameters that reduce its classification error in response to these adversarial attacks. This work was extended by the authors to calculate the Nash equilibrium of a constant-sum game efficiently with reduced computation~\cite{Liu12}, and, recently, with the learner using a deep convolutional neural network as its classifier~\cite{Chivukula17}.  Nevertheless, several authors~\cite{Huang11,Bruckner11} justify the learner as the leader while observing that most real-life classifier-based systems like email spam filters and network traffic filters publish their classifier algorithms publicly.

In contrast to sequential move games, simultaneous move games have been used less often to model interaction between an adversary and the learner. Recently, Schuurmans and Zinkevich~\cite{Schuurmans16} have addressed the problem of training a deep neural network as solving for a Nash equilibrium in a repeated, zero-sum game between two players, called protagonist and antagonist. The antagonist's objective is to determine a set of parameters that reduces the loss function during training, while the protagonist tries to select weight values of the edges in the deep network such that its utility is the negative of the antagonist's utility. Additional players called zannis are placed at the input and hidden layer nodes to select those nodes' parameter values. Iterations used to adjust weights of the neural network's edges in a conventional supervised training algorithm are modeled as repeated plays of the game aimed at converging to the Nash equilibrium and implemented using two algorithms called exponentiated weight and regret matching. Although not directly related to an adversarial setting where the adversary generates adversarial data to misguide the learner's classification, the deep learning game provides an interesting direction that is amenable to be extended to an adversarial setting.

\subsection{Non-zero Sum Games: Bayes-Nash Equilibrium and Related Solution Techniques}
\label{sec_nonzerosumgames}
Non-zero sum games have been proposed as a more realistic model for the interactions between the learner and adversary in adversarial learning~\cite{Bruckner11,Groshans13,Groshans15,Mei15,Alfeld17} because the loss in utility of learner might not exactly equal to the gain in utility of the adversary, and vice-versa. In a non-zero sum adversarial learning game, the learner calculates its loss in utility as proportional to the number of data instances on which it made a classification error because the data was modified by the adversary. However, a wrinkle to this approach is that the learner does not know whether the adversary had indeed modified the data to make it commit a classification mistake. For example, consider a learner in an automated email spam filter that has a spam identification rule as: 'if there are more than three misspelled words in an email text, then the email is spam'. Suppose that this learner receives an email that has five misspelled words and classifies it as spam following its spam identification rule. However, the learner does not know if the misspelled words were generated by an adversary, or, whether, they were genuine typographic errors made by a human. To address this problem, the learner tries to estimate probabilistically, if an instance it classifies was generated by an adversary or not, and, weighs its classification error by this probability. Correspondingly, the adversary also calculates its loss in utility depending on whether its adversarially generated instance was able to fool the classifier or not. Using this framework, Bruckner {\em et al.}~\cite{Bruckner11} proposed a non-zero sum game to model adversarial learning. The pairs of utilties of the learner and the adversary form a probabilistic version of the normal form game called a Bayesian game. The strategies adopted by the players in this Bayesian game, for example, the classifier hyperparameters selected by the learner and the perturbation strategy to modify legitimate data selected by the adversary, is calculated using the Bayes-Nash equilibrium.

Like the ACRE algorithm~\cite{Lowd05}, Bruckner's model~\cite{Bruckner11} has also been extended from different aspects in future research. In ~\cite{Mei15}, authors considered attacks on the training set in adversarial learning within a problem called machine teaching. Here, the adversary takes the role of a teacher while the learner takes on the role of a student whose objective is to learn a concept from data provided by the teacher. The objective of the teacher is to make causative attacks discussed in the background section of our article, so that it can coerce the learner towards learning a concept that it desires. Like~\cite{Bruckner11}, the problem is modeled as a bi-level optimization problem and solved by relaxing it to linear optimization using Karush-Kuhn Tucker(KKT) conditions. Subsequently, Alfeld {\em et al.}~\cite{Alfeld17} extended this framework to test time attacks on data. Bruckner's model~\cite{Bruckner11} has also been generalized in~\cite{Bulo16} using randomized prediction games where the learner's prediction algorithm randomizes over classifiers with different weight parameters while the adversary randomizes over its adversarial vectors.

Another direction of adversarial learning investigated by Zhou {\em et al.}~\cite{Zhou12} is along the lines of Globerson's model of selective feature removal~\cite{Globerson06}. Here, the authors consider two adversarial attack models called full range attacks and restrained attacks. In a full range attack, the adversary can perturb any fraction of the maximum range of a feature. On the other hand, in a restrained range attack, the adversary can perturb only a fraction of the difference between its intended value and the actual value of a feature. The learner's prediction mechanism to counter these attacks is an SVM-based classifier that minimizes hinge loss. Extending this work towards more robust learner, the authors proposed a mixture of Bayesian experts approach~\cite{Zhou14} as the learner's prediction mechanism.

An adversarial learning game called a classification game in~\cite{Dritsoula17} considers a practical adversarial strategy used by adversaries like spammers. Spammers might sometimes behave non-maliciously and not generate adversarial data to misguide the learner. This could result in false alarms by the learner if it incorrectly identifies the adversary as malicious when it is not. To account for this, the learner maintains a probability of the adversary being malicious versus non-malicious. The game is non-zero sum as the learner's utility includes the negative of the adversary's utility when it is a malicious, plus the learner's expected penalty from false alarms. Their work analyses the existence and uniqueness of Nash equilibrium for this classification game and proposes a constrained optimization solution, solved as linear program, to calculate the Nash equilibrium. Their model is validated by generating numeric values of learner and adversary costs, and size of strategy sets when data instances have either single or multiple features. Their results show that the learner utility decreases while attacker utility increases when either the cost of a single attack or the false alarm penalty increases.

\section{Learner Robustness via Adversarial Data Modeling}

The game theory-based adversarial learning techniques discussed thus far mainly focus on strategies that the learner could use to develop robustness against attacks from an adversary. Another approach to build the learner's defense mechanism, although not based on game theory, focusses on modeling the malicious data generated by the adversary so that the learner could understand the nature of adversarial attacks. Armed with information about the characteristics of adversarial attacks, the learner could then build appropriate defenses, such as train its classifier with the adversarial data, to improve robustness against the adversarial attacks. In this section, we provide an overview of three popular techniques for adversarial data generation that could be used in conjunction with adversarial learning. These techniques include adversarial data generation using perturbation techniques on valid examples, transferring adversarial examples across different learner models, and generative adversarial networks.

\subsection{Adversarial Data Generation via Perturbation}
Before building the learner's defense mechanism against adversarial attacks, a first line of defense towards protecting against attacks is to understand how the adversary crafts those attacks. The topic of adversarial data generation seeks to address this issue by developing and analyzing different techniques that create synthetic, adversarial data, which could be used by potential adversaries. In most of these techniques, an adversarial example is constructed by adding a certain amount of noise or perturbation to a valid example. The main objective while creating an adversarial example is to perturb a valid example so that the perturbation is imperceptible to a human; in other words, the perturbed example appears to have the same label as a valid example to a human. However, when presented to a machine classifier, the same perturbed example would be assigned a different label than the valid example's label. For example, in a spam filtering scenario, when a perturbed example is created from a valid spam email message, the perturbed example would still appear to be a spam message to a human, but a spam filtering classifier would categorize it as non-spam, and, vice-versa. To achieve this property of imperceptibility to humans but deception for machine classifiers, the perturbation added to a valid example should take the perturbed example {\em just} across a decision boundary of the machine's classifier. Too little of a perturbation prevents the perturbed example from crossing the decision boundary - the perturbed example appears valid to the human, but does not fool the classifier either. On the other hand, too much perturbation takes the perturbed example far across the decision boundary, the classifier does not classify it correctly, but the excessively perturbed example appears as nonsense or rubbish~\cite{Goodfellow14-1} to a human who can easily discern it as an adversarially perturbed example. The main problem in adversarial data generation is then to determine this suitable amount of perturbation.

In one of the earliest works in this direction, Biggio {\em et al.}~\cite{Biggio13} proposed a gradient descent technique that used the gradient of the discriminant function of the classifier along with the density function of the data to calculate a suitable amount of perturbation and generate a perturbed example. The proposed technique was validated to generate adversarial data from valid examples of handwritten digits and portable document format (PDF) text files while using different machine learning classifiers including linear classifiers, support vector machines and neural network classifiers. Optimization-based algorithms to determine the minimum amount of perturbation have been proposed in~\cite{Szegedy13}, ~\cite{Carlini16}. Goodfellow {\em et al.} in~\cite{Goodfellow14-1} made several fundamental contributions towards understanding properties of perturbations that create adversarial examples as well as properties of learner models that make them susceptible to adversarial examples. They proposed a fast, simple yet effective perturbation technique called Fast Gradient Sign Method (FGSM) to add perturbation proportional to the gradient of the cost function (e.g., loss function) to classify an example in a deep neural network. Their work also made valuable observations about perturbation techniques such as the direction of perturbation rather than amount of perturbation is more critical in creating adversarial examples, training a classifier with adversarial examples is akin to regularization of the classifier, and that a positive correlation exists between the degree to which a learning model can be optimized and its susceptibility to perturbation. Building upon these directions, researchers have proposed more refined perturbation techniques such as perturbing the label that has the lowest probability for the valid example in a single or multiple steps~\cite{Kurakin16-1}~\cite{Kurakin16-2}, perturbing an example's features that are most likely to change the classifier's output based on forward gradients~\cite{Papernot16}, universal peturbations to determine the minimal perturbation that will generate a certain fraction of adversarial examples guaranteed to result in misclassification when the examples are drawn from a given data distribution~\cite{Moosavi16} and neural networks called adversarial transformation networks that are trained to create adversarial examples~\cite{Baluja18}. Most of the aforementioned techniques have been proposed for generating adversarial data of handwritten digits or images. In contrast, Sethi and Kantardzic described methods to generate adversarial text data. Adversarial examples called attack data are constructed from probing the classifier with randomly perturbed text and retaining the perturbations that are successful in fooling the classifier, when the number of probes that the adversary can make is limited. Without limits on the number of probes, the adversary can reverse engineer~\cite{Lowd05} the classifier to create more precise advesarial examples that are able to fool the classifier more often~\cite{Sethi18}. In~\cite{Carlini18}, authors have recently proposed methods for generating adversarial audio data for misleading speech-to-text machine classifiers. In general, the topic of adversarial data generation techniques to gain better insights into how the adversary can deceive the learner's classifier with malicious data is still an open research problem that requires meticulous analysis of data perturbation techniques in conjunction with the characteristics of the model used by the learner for classification.

\subsection{Transferring Adversarial Examples}
The adversarial data generation techniques discussed in the previous section require the virtual adversary to utilize the model used by the learner to classify examples so that the virtual adversary can determine whether the adversarial examples it generates are able to deceive the learner's classifier. Because the virtual adversary cannot gain access to the learner's model, the adversary usually resorts to reconstructing the learner's model via probing - sending valid and adversarial examples to the learner's model of the classifier and observing the output label assigned by the classifier. Each probe incurs a cost for the adversary because the adversary has to expend resources to acquire valid examples and perturb them, plus the learner could limit the number of examples the adversary could send. To reduce costs, it would be beneficial for the adversary if it could generate adversarial examples to fool a classifier while utilizing a certain learner model, and then reuse those same adversarial examples to fool multiple, different classifiers. This technique of sending adversarial example generated using one learner model to a different model for classification is called {\em transferring} examples. The transfer problem is very relevant in the context of cybersecurity because it affords adversaries a low-cost technique to attack diverse machine learning-based classifiers such as email spam filters, network intrusion detection systems, identity authentication systems, etc., presumably at different locations while generating only one set of adversarial data.

As in the case of adversarial data generation, research in transferring adversarial examples has focused mainly on what characteristics of learner models of classifiers favor transferring adversarial examples across the models. In~\cite{Goodfellow14-1}, authors identified that when the weight vectors of two neural network-based learning models are aligned with each other, adversarial examples generated using one model could be transferred to the other. The transferability of adversarially generated image data across different learning-based models of image classifiers was investigated in~\cite{Liu16} to discover that the alignment of the decision boundaries of different models favors transferability of adversarial examples across models. Recently, Tramer {\em et al.}~\cite{Tramer17-2} investigated the dimensionality of adversarial subspaces as a means to determine the transferability of adversarial examples. They concluded that the subspace of adversarial examples has a large dimension (about $25$) and adversarial examples are transferable across two learner models when there is significant overlap in the subspace of the adversarial examples generated using the two models. Based on these findings the same authors proposed a technique called ensemble adversarial training~\cite{Tramer17-1} where perturbations generated using one learner model are transferred or informed to another learner model to make the latter model more robust to adversarial examples. Like adversarial data generation, transferability of adversarial examples between different learner models is also an open problem whose investigation could lead to more robust adversarial learning techniques.

\subsection{Generative Adversarial Networks}
Generative Adversarial Networks (GANs)~\cite{Goodfellow14} have recently been proposed as a game theory-based techniques to simultaneously generate perturbed examples from an adversary and then use those adversarial examples to train the learner's model. In a GAN, the learner uses a function called the discriminator (usually a classifier) while the adversary's function is called the generator. The interaction between the discriminator and the generator is modeled as a zero-sum game, and both the discriminator and generator iteratively solve a minimax optimization function. The optimization is done iteratively over chunks or batches of data and implements a gradient descent over the respective loss functions of the discriminator and generator. Experimental results of this approach show that both discriminator and generator are able to continuously adapt their prediction and data corruption mechanisms respectively.  Rather than improving the robustness of the learner to adversarial examples, the main contribution of GANs has been to demonstrate that the adversary (generator) can create very convincing adversarial or counterfeit examples, e.g., images of animals, human faces, traffic signs, etc. that are not distinguishable from a real image by the human eye, but can cause the discriminator to output an incorrect classification. For example, a picture of a cat could be modified by the generator in a way that is imperceptible to the human eye but causes the discriminator to mis-label it as an airplane. More generally, the GAN techniques have shown for the first time that data labeling, which is largely a supervised learning task utilizing labeled training data from humans, can also be implemented as an unsupervised learning task by exploiting the adversarial attacks of the generator. GANs have been used in several applications including image, audio and video generation, computer vision tasks such as image and video labeling~\cite{Vondrick16,Reed16}. Improvements of the basic GAN including Wasserstein GAN~\cite{Arjovsky17}, SeqGAN~\cite{Yu17}, StackGAN~\cite{Zhang16}, and EnergyGAN~\cite{Zhou16} have also been proposed. Defense mechanisms of the discriminator in a GAN include adversarial training, defensive distillation and gradient masking~\cite{Papernot17}, as well as statistical data analysis methods~\cite{Grosse17}, although many of these techniques have been shown to be vulnerable more recently~\cite{Carlini16},~\cite{He17}. Similar to perturbation-based adversarial data generation, techniques developed for GANs to generate adversarial examples could also be used towards developing stronger adversarial learning mechanisms.

\section{Open Problems and Further Directions}
\label{sec_future}
As discussed above, game theory offers a convenient means of modeling learner and adversary behaviors in adversarial learning. However, with recent developments in game theory based behavior modeling using repeated, evolutionary games and machine learning using deep networks, there are certain directions in which these models could be improved to make adversarial learning more robust against real-life adversarial threats. We identify some of these potential directions for future research below:

{\bf Richer models of learner and adversary behavior.} A shortcoming of many existing game theory based models of adversarial learning is that the learner uses only one-time interaction history with the adversary to build and update its model of the adversary's behavior of generating adversarial data instances. We envisage that a more complex model, that considers the history of interactions between the learner and adversary can enable the learner to make more accurate decisions of the adversary's behavior and adapt its classifier accordingly. Repeated games with Bayesian learning provide a theoretical foundation for building such a history-based model of the adversary by the learner. Investigating this direction, while developing fast, heuristics-based algorithms that can guarantee accurate prediction of the adversary's behavior within quantifiable bounds, would lead towards more accurate and robust models adversarial learning. Below, we identify some specific directions and open problems in adversarial learning.

{\bf Bounded life-force of adversary and learner.} Most of the game theory-based adversarial learning models discussed above assume that the adversary has unlimited resources (e.g., access to valid data, Internet access), and, budget to craft adversarial examples. Taken together, these assets could be considered as a {\em life force} of the adversary. However, in real-life, Internet adversaries such as spammers, usually have limited life force within which they attempt to maximize the impact of the adversarial data they generate. Recently, researchers have started exploring this direction by considering a bounded feature adversary~\cite{Park17} that is limited in the extent of change it can effect on features in valid data and on the number of queries it can make to the learner~\cite{Globerson06,Hardt16}. An interesting and practical future direction that has been less investigated is the effect diminishing life force on the strategy of the adversary. For example, an adversary that perceives very little remaining life force would adopt aggressive strategies to maximize its harm on the learner. Going a level deeper, the learner could also build a model of the adversary's life force by analyzing the adversary's attacks and strategize its defense mechanism to minimize harm. Repeated game frameworks with that incorporate life force-based strategizing offer an suitable direction to investigate this problem.

{\bf Diminishing value of shared resources (data set).} Yet another limitation of existing adversarial learning models is that the value of shared resource, e.g., email data, is considered to be immutable while being subject to adversarial attacks. In reality, due to non-zero error rates of the learner's classifer, a small, but non-negligible amount of causative and exploratory attacks get past the classifier, giving rise to a compromised data set and reduce reliability of the classifier. It would make sense to investigate techniques that incorporate diminished value of data in training and test, and its effect on the classifier confidence, in the defense mechanism used by the learner. Once again, the adversary could simultaneously attempt to model the value of the data and the classifier confidence, and incorporate these metrics into the adversarial data generation strategy.

{\bf Tactical defender and attacker strategies.} As discussed earlier, game theory based adversarial learning models use learner and adversary utility values to parameterize strategies. In real-life, adversaries like spammers of Website attackers use tactical strategies for their attacks. Example learner strategies could include guns or butter, growing soft, strict justice, layered defense, while the attacker could strategize with low but slow attacks, surprise attack, David and Goliath-type attack, suicide attack. Techniques from behavioral game theory provide a suitable framework for modeling such tactical strategies and bringing adversarial learning research closer to practical attacks.

{\bf Deeper behavior modeling by adversary and learner}. Most existing techniques for adversarial learning are based on a sequential game where the adversary has information about the classifier used by the learner, although it does not have information about the parameters of the classifier. An interesting and practical direction worthy of investigation is a game theory-based, adversarial learning model where the adversary has only partial, possibly inaccurate information about the learner's classifier. This setting would be relevant in most real-life settings, as the classifier used by the learner is usually proprietary information that is confidential to the learner. Similarly, moving beyond Nash equlibrium, solutions like the price of anarchy, and regret minimization~\cite{LeytonBrown09} could provide faster means of calculating strategies by the learner and adversary. Related to this direction, game theory models could be made more informed by incorporating modeling and reasoning costs such as cost to solve for Nash equilibrium, cost to maintain game play history and build opponent models from the history. Similarly, expenses incurred by the adversary to get access to resources like legitimate email data sets and to the learner's classifier could be modeled as a reward it gets, that is proportional to its success in compromising the learner.

{\bf Robust classification with sparse data.}  Supervised learning algorithms, including support vector machines, regression learning, etc., that are used to build the learner's classifier in adversarial learning rely heavily on large, information rich, training sets to predict correctly. In many instances, these algorithms suffer from low accuracy if the data used in training is sparse or does not contain all possible feature instances. To mitigate this problem, a technique called domain adaptation or transfer learning has been proposed in literature. However, to the best of our knowledge, transfer learning has not been investigated in the context of adversarial learning. Using transfer learning would be very relevant in adversarial learning with sparse training data. For example, the mapping determined by the classifier from input instances to class labels for helpful vs. not-helpful movie reviews in a movie reviews data set (e.g., IMDB data set), could be re-used, after suitable transformations, for classifying Internet clients as malicious vs. non-malicious from sparse, server log data. The critical problem here is to find correspondences between datasets between the source and target domains, and then suitably adapt the mapping learned in the source domain to the target domain.

\section{Conclusions}
In this paper, we have provided a systematic classification of adversarial learning techniques using game theoretic frameworks. While adversarial learning has been researched for over a decade, recent advances in machine learning, especially with deep networks, could be used to enhance existing game theory techniques for deeper learner and adversary behavior modeling, as well as, to compute more efficient and robust action selection strategies by the learner. We have identified several open problems and challenges for future research along these direcitons. With the recent, phenomenal growth of machine learning-based intelligent systems, we believe that addressing these challenges will advance real-life, classifier-based learning systems like email spam classifiers, social network sentiment analysis tools, and image and sensor data recognition systems on autonomous vehicles towards becoming more robust and reliable for seamless human use.

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Friends of Point Pelee is currently accepting applications for our Marsh Services Attendant position. This position is seasonal starting May 17th through to Labour Day Monday. The position involves some Retail Sales (snacks, drinks and souveniers) as well as Canoe Rentals.


Milky River playful childish font contains the uppercase and main & alternative lowercase letters. And also the full set of double letters a large range of numerals and punctuation. The Milky River font will be perfect for use in all your fun design projects be it logos, labels, packaging design, blog headlines. Also, it will look great on mugs, cards, kids books headlines or other typographic projects.


text:
Submitted by Jesse Bell on Sep 01, 2018.
Denim vests and cutoff denim shorts, trucker hats and chewing tobacco. Small cars, big trucks, mud and dust all over your face, your hands, your feet, your clothes (if you happen to be wearing any). This, all of this, is Derby.
Derby Day in Fernie is by all rites a national holiday, an elaborate and wild celebration at the end of the summer, a Labour Day Sunday always worth attending. Everyone—despite their geographical background or personal preference for exploits—comes together.
Families fill bleachers and others pack into beer gardens. People spend weeks and months fixing up old cars to drive them into the stadium to be destroyed. A hot dog becomes our best friend, a glass of water our enemy.
And when the cars and trucks smash, we yell. We yell so loud we almost drown out the revving engines and our voices become hoarse. We choke on the dust, and raise overflowing beer-filled cups.
I love Derby.
When I was a kid I'd stand on the second rung on the gates at gymkhana and watch rust buckets collide. I like to think I always appreciated the excitement of it all, but it wasn't until I moved home that I finally understood.
If you haven't been to Derby, you're missing out on Fernie. Real Fernie. Nitty, gritty, get-filthy Fernie. And it's all for a good cause.
The annual Lions Club fundraiser has been running Derby Day for 40 long years, and each year seems better than the last. The smash, bash, crash-athon began in Hosmer in the 1970s, but coal dust pushed the event here, and in 2012 it moved to an open field with the Lizard Range as background, gophers chirping in the grass nearby.
To date, the event has raised nearly half-a-million dollars for local charities, families, youth sports and other community causes.
I can't think of a better reason to go.
Rick Mitchell remembers when Derby started—he was just ten years old. Nearly every year since he's been involved, fixing up old trucks and cars, entering the stadium prepared to hit the gas, the number 66 painted on the car's side. His kids became a part of the event, and now his grandkids too; a Derby tradition for three generations.
"It's the thrill when you're out there," he says. "It's just awesome, you don't know where the hit's coming from, but you're goin' to get hit."
Rick's won several derbies, both for best dressed and the main event, but it's not only winning he considers to be the best part about it. It's the crowd, the reactions, the beer-filled cheers and crash-induced screams, it's the reaction to a rollover.
"I've never rolled over, but I'm still trying," says Rick. "I always paint something on the bottom of the car; get a rollover and the crowd goes nuts. Anything you can do to please the crowds, that's what we're there for. We're there to entertain."
Entertain indeed. Since its inception more than 1000 cars wound up punished and mulched, and thousands of people have found themselves fully immersed in the chaos. I haven't missed a Derby since moving home seven years ago. First it was Derby brunches with unfamiliar friends—now it's Derby Day with my best friends. Denim clad head-to-toe, there's a sense of nostalgia with each flick of mud and crunch of bumper.
Derby and river swims, the greatest sense of community, a little car-crashing education.
Admittedly, I look over the fence that separates the beer gardens from the competitors with envy. Clad in full coveralls, the people who put time and money into a few short minutes of thrill and adrenaline are some pretty incredible people. I've contemplated the Powder Puff event, but I'm just not badass enough.
Each year I raise a glass to them. Okay, okay. Several glasses, some hotdogs, vulgar yelling and terrible dance moves.
"It's one of the greatest fundraisers in the valley," says Rick. I couldn't agree more.
This Labour Day Sunday, you know where I'll be—at Derby.
The Lions Demolition Derby runs Sunday, Sept. 2 from 11-5. Tickets are $10 for adults, $5 for kids, with a 50-50 draw and over $4000 in prizes to be won. Visitfernielionsclub.ca or more information. And, if you're lucky, I'll see you at Derby.
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text:
it will depend at what stage of the proceedings you have reached and what orders have already been made. A child can only be adopted when three orders have been made – a care order, a placement order and finally, an adoption order. Care and placement orders are usually made at the same time.
A care order allows the State to decide where your child should live and who spends time with him or her.
A placement order allows the State to put your child with a family that may decide to adopt him or her.
An adoption order confirms that this family is now the legal family and the birth parents no longer have any legal connection to their child.
So which situation are you in? This post will discuss only the LAST TWO. If you want to challenge a care order – see this post.
Parents are currently in care proceedings and no final order has been made It is really important that parents argue their case in the care proceedings while they are happening – you need to engage with the case against you at the time as it may be too late to do anything to change the situation once a care order is made.
Final care order made but no placement order. If a placement order hasn't been made yet, you may be able to appeal against the care order or apply to discharge it.
Final care order and placement order made – Parents can apply for leave to revoke a placement order under section 24 of the ACA 2002, IF:
their child hasn't yet been placed for adoption; and
they can show a 'change of circumstances' since the placement order was made.
The form to make an application to revoke a placement order is here.
Potential adoptive parents have applied for an adoption order – Parents can apply for permission to contest the making of an adoption order under section 47(7) of the ACA 2002 but only if they can show a 'change of circumstances'.
The adoption order has been made – can I overturn it? – this is rare but possible. See discussion below.
Challenging an application for an adoption order
Don't waste time
Remember – UK law is compatible with the ECHR
It is always better to make your challenges and objections during the care proceedings. It is essential to challenge a care order as soon as possible if you do not consider it was validly made – it is too late to wait until the time that applications are made to apply for adoption.
Don't waste time arguing that UK law is not compliant with international law. In the case of G (A Child) [2017] EWCA Civ 2638 (08 November 2017) the father wanted the court to declare that the Adoption and Children Act 2002 was not compatible with the European Convention on Human Rights. The Court of Appeal referred to Re CB (A Child) (No. 2) (Adoption Proceedings: Vienna Convention) [2016] 1 FLR 1286 in which Sir James Munby President said at paragraph 83:
"The second point is that, whatever the concerns that are expressed elsewhere in Europe, there can be no suggestion that, in this regard, the domestic law of England and Wales is incompatible with the UK's international obligations or, specifically, with its obligations under the European Convention for the Protection of Human Rights and Fundamental Freedoms 1950. There is nothing in the Strasburg jurisprudence to suggest that our domestic law is, in this regard, incompatible with the Convention. For example, there is nothing in the various non-consensual adoption cases in which a challenge has been mounted to suggest that our system is, as such, Convention non-compliant."
If you are arguing about the care proceedings when faced with adoption order – you've already lost
There is a useful and clear discussion about the process in the case of A and F (children) [2015] where a mother argued that her children should not be adopted but this argument was based on an assertion that the care orders should not have been made in the first place. Therefore, she did not accept that any criticism could be made of her parenting and she was unable to engage with the essential steps to challenge her children's adoption – that she must show 'a change of circumstances'
As the Judge commented at para 26 of his judgment:
Indeed, the majority of the mother's statement is concerned with the repetition and correction of perceived past wrongs sustained by her. This was also the position with regard to her oral submissions. This means that inevitably she does not accept as a "starting point" District Judge Shaw's decision nor his findings. As a matter of logic, therefore, she finds it impossible to address the issue of "changes in circumstances" because broadly her parenting circumstances, when the children were removed, were perfectly acceptable and therefore no change is required. Accordingly, an intellectual impasse results.
So what do you have to do?
Step One: establish a change of circumstances. The court has already decided by making a final care order that the parent has caused or is likely to cause a child significant harm. Therefore the parent must show the court what is different NOW. This is discussed in more detail below;
Step Two: convince the court it is right to give permission to argue against an adoption order being made. This means that the court will look at all the relevant issues in the case and think about what the impact would be on the children. The children's welfare is the most important consideration for the court. If the parent doesn't succeed in getting permission, the matter ends there.
Step Three: Persuade the court to refuse an adoption order IF a parent is given permission to argue against the making of an adoption order, they will have to persuade the court to reverse the direction in which the children's lives have travelled since the Care and Placement proceedings. Obviously, the longer the children have been in their potential adoptive placement, the harder this will be.
Although the courts try to separate out the different questions, to make it easier to analyse the issues, it is clear that each question has the potential to be significantly wrapped up in the other questions. For example, the 'prospect of success' the court is looking at refers to your prospect of success in challenging the order, NOT your prospects of success in getting your child home.
However, if you have very little chance of persuading the court that the child should come home, that issue is certainly going to be on the court's mind. It is very difficult to successfully challenge placement or adoption orders, as by the time such challenge is made the child has been living away from the parents for many months, even years and the court is going be worried about the impact on the child of possibly another move from a home where they may now be settled.
In Re L [2014] 2FLR 913 at paragraph 45, Lady Justice Black said this:
"When a judge considers a parent's prospect of success for the purposes of section 47(5), he is doing the best he can to forecast what decision the judge hearing the adoption application is going to make having the child's welfare throughout his life as his paramount consideration. What is ultimately going to be relevant to the decision whether to grant the adoption order or not must therefore also be material at the leave stage."
STEP ONE – what does 'change in circumstances' mean?
It's a matter of fact and it has to be relevant. Case law gives us the following principles :
The test should not be so difficult that it rules everyone out – parents shouldn't be discouraged from trying to improve their lives.
The changes must be relevant to the question of whether or not leave should be granted – for e.g. if the worry was originally that you drink too much, have you stopped or cut down?
The changes are not confined to those of a birth parent, but they may include changes occurring in the child's life (see Re T [2014] EWCA (Civ) 1369).
The necessary change in circumstances … does not have to be "significant"; the question is whether it is "of a nature and degree sufficient, on the facts of the particular case, to open the door to the exercise of the judicial discretion to permit the parents to defend the adoption proceedings": Re P (Adoption: Leave Provisions) [2007] EWCA 616, [2007] para 30 – discussed in Re T [2014] in context of applying to revoke a placement order.
There is a useful article here by suesspiciousminds which considers the relevant case law in this area, and in particular the case of The Borough of Poole v W [2014] EWHC 1777. The Judge concluded at paragraph 25 of his judgement that the parents could not succeed, despite making considerable changes to their lives:
I have considered this case with the most anxious care, considering how much is at stake, both for parents and prospective adopters who happily all have a real understanding of each other's predicaments. However, above all what is at stake for SR? There can be no blame attached to any of the four adults for why we have all ended up where we have. Nevertheless, a decision of profound significance has to be made. In the end, I have reached a clear conclusion that there is only one route which will sufficiently safeguard the welfare of SR and that is the route of adoption.
My real concerns about SR's ability to survive the process of rehabilitation and the parents' ability to sustain her care, whatever her reactions throughout her childhood, when seen in the context of their fragility and of the consequences to SR of a failure of rehabilitation and the need to then start all over again. All those matters when drawn together, in my judgment, require that adoption be provided as the way of securing her welfare and therefore require that the court dispenses with the parents' consent. In making the order which, in my judgment, promotes the welfare of SR, I fully recognise the grief of the parents who do not share my view and I recognise that I have no comfort to offer them, beyond letterbox contact. If ever an example was needed of how legitimate and heartfelt aspirations of parents can be trumped by the welfare needs of the child, this surely is it.
For an example of a case where a mother succeeded in appealing against the initial refusal to allow her to argue against a placement order, see the case of G (A Child) [2015] EWCA Civ 119, discussed in this post by suesspiciousminds. The Court of Appeal agreed that a change to the child's circumstances could also be relevant:
The "change in circumstances" specified in section 24(3) of the 2002 Act is not confined to the parent's own circumstances. Depending upon the facts of the case, the child/ren's circumstances may themselves have changed in the interim, not least by reason of the thwarted ambitions on the part of the local authority to place them for adoption in a timely fashion. I would regard it as unlikely for there to be many situations where the change in the child's circumstances alone would be sufficient to open the gateway under section 24(2) and (3) and I do not suggest that there needs to be an in-depth analysis of the child/ren's welfare needs at the first stage, which are more aptly considered at the second , but I cannot see how a court is able to disregard any changes in the child/ren's circumstances, good or bad, if it is charged with evaluating the sufficiency of the nature and degree of the parent's change of circumstances.
The case of P (A Child) [2018] EWCA Civ 1483 (28 June 2018) allowed a mother's appeal against the refusal to grant her an adjournment before making a placement order. Although there had been long standing concerns about her alcoholism, she had developed considerable insight and made significant progress – she had done 'all' that could be expected of her. The Court of Appeal rejected the suggestion that a six month adjournment served 'no purpose' given that the plan for a 6 month old baby was adoption.
Further reading about 'change of circumstances'.
See this post from the UK Human Rights blog – Challenging Adoption order using Human Rights.
For an example of a case where an appeal against an adoption order did not succeed, see R (A Child) [2014]
For a case discussing what happened when a parent wanted to contest an adoption order by asking the court to look at fresh evidence, see the case of G (A Child) [2014].
See C (Revocation of Placement Orders) [2020] EWCA Civ 1598 (27 November 2020) and discussion of the case by The Transparency Project. Even a mother who was said to have made a 'remarkable transformation' to her life did not succeed in challenging the adoption of her three children.
STEP TWO: If there is a change of circumstances, should the court give you permission to challenge the adoption order?
In relation to Step two this an issue of judicial evaluation or discretion which means that different judges can and do make different decisions but could not necessarily be challenged on appeal. 'Exercising a discretion' means you are making your own value judgment and there is usually a pretty wide range of possible outcomes that would be accepted. Provided of course that the Judge has applied the correct law and facts.
The parent must have 'solid grounds' for making the application. Paragraph 74(i) to (x) of Re B-S identifies the features to be weighed in the balance.
Prospect of success here relates to the prospect of resisting the making of an adoption order, not the prospect of ultimately having the child restored to the parent's care.
The issues of 'change in circumstances' and 'solid grounds for seeking leave' are treated as two separate issues in order to analyse them BUT in reality they are inter-linked and one may follow the other
If the Judge finds a change of circumstances AND solid grounds for seeking permission, the Judge must then consider child's welfare very carefully.
The judge must keep at the forefront of his mind the teaching of Re B, in particular that adoption is the "last resort" and only permissible if "nothing else will do" and that, as Lord Neuberger emphasised, the child's interests include being brought up by the parents or wider family unless the overriding requirements of the child's welfare make that not possible.
But, the child's welfare is paramount.
To find out what the child's welfare needs, the judge must take into account 'all the negatives and the positives, all the pros and cons, of each of the two options, that is, either giving or refusing the parent leave to oppose. The use of Thorpe LJ's 'balance sheet' is to be encouraged.
The court needs proper evidence, but this doesn't always have to be evidence from people speaking to the court. Often applications for leave can be fairly dealt with on written evidence and submissions.
As a general proposition, the greater the positive change in circumstances and the more solid the parent's grounds for seeking leave to oppose, the more significant must be the detrimental impact on the child if the court is going to refuse to give them permission to challenge the adoption order.
The fact a child is now living with the prospective adopters or that a long time has passed, cannot determine the matter.
BUT the older the child and the longer he/she has been living with the prospective adoptions, the worse it is likely to be to disturb that.
The court should not attach too much weight to any argument that the proceedings are having an adverse impact on the prospective adopters – but this isn't a trivial point and judges must try to minimise this impact by robust case management.
The judge must always bear in mind that what is paramount in every adoption case is the welfare of the child "throughout his life".
Given modern expectation of life, this means that, with a young child, one is looking far ahead into a very distant future – upwards of eighty or even ninety years. Against this perspective, judges must be careful not to attach undue weight to the short term consequences for the child if leave to oppose is given. In this as in other contexts, judges should be guided by what Sir Thomas Bingham MR said in Re O (Contact: Imposition of Conditions) [1995] 2 FLR 124, 129, that "the court should take a medium-term and long-term view of the child's development and not accord excessive weight to what appear likely to be short-term or transient problems." That was said in the context of contact but it has a much wider resonance: Re G (Education: Religious Upbringing) [2012] EWCA Civ 1233, [2013] 1 FLR 677, para 26.
The court will be well aware of the seriousness of adoption and the decision of the Supreme Court in the case of Re B [2013] 1WLR 1911.
See also W (A Child: Leave To Oppose Adoption) [2020] EWCA Civ 16 (21 January 2020) where the appeal court agreed the parents should be given permission to argue against the making of an adoption order.
STEP THREE: Will the court reverse the 'direction of travel' for the child and refuse to make an adoption order?
It is quite rare for the court to refuse to make an adoption order. One example of such a case is A and B v Rotherham Metropolitan Borough Council [2014] which is the first since the 2002 Adoption Act. The court removed the child from the home of the potential adoptive parents – where he was settled – to live with his paternal aunt. It is clear that the court must consider the child's welfare throughout his life – as the Judge commented here, this could mean 80 years or more.
The Judge commented at paragraph 95:
This case clearly requires taking both a short term and a long term view. C is currently very well placed with "perfect adopters". They are a well trained couple with whom he is very well attached. He is of mixed race. They are both white and share with him that half of his ethnicity. A and B are "tried and tested" as has been said. His aunt and the principal members of the paternal family are black and share with him that half of his ethnicity. The aunt is a single person. She has not been "tried and tested" as a carer for C, but she has been observed as a carer of her own child, G, and thoroughly assessed as entirely suitable to care long term for C. There would be likely to be short, and possibly long term harm if he now moves from A and B to the aunt, but that is mitigated by his embedded security and attachments with A and B, and can be further mitigated by specialist training and support for the aunt, which she will gladly accept. The unquantifiable but potentially considerable advantage of a move to the aunt is the bridge to the paternal original family.
It is my firm judgment and view that it is positively better for C not to be adopted but to move to the aunt. In any event, I certainly do not consider that making an adoption order would be better for C than not doing so. Accordingly I must, as I do, determine not to make an adoption order and must dismiss the adoption application. Pursuant to section 24(4) of the Act, I exercise a discretion to revoke the placement order made in respect of the child on 2 August 2013.
The Judge had this to say about the 'nothing else will do' test at paragraph 15:
With so many Article 8 rights engaged and in competition, it does not seem to me to be helpful or necessary in the present case to add a gloss to section 1 of only making an adoption order if "nothing else will do"… Rather, I should simply make the welfare of the child throughout his life the paramount consideration; consider and have regard to all the relevant matters listed in section 1(4) and any other relevant matters; and make an adoption order if, but only if, doing so "would be better for the child than not doing so", as section 1(6) requires.
If the balance of factors comes down against making an adoption order, then clearly I should not make one. If they are so evenly balanced that it is not possible to say that making an adoption order would be "better" for him than not doing so, then I should not do so. If, however, the balance does come down clearly in favour of making an adoption order, then, in the circumstances of this case, I should make one. I do not propose to add some additional hurdle or test of "nothing else will do".
The decision of the Court of Appeal in July 2016 in W (A Child) [2016] EWCA Civ 793 dealt explicitly with four very important questions:
The approach to be taken in determining a child's long-term welfare once the child has become fully settled in a prospective adoptive home and, late in the day, a viable family placement is identified;
The application of the Supreme Court judgment in Re B [2013] UKSC 33 ("nothing else will do") in that context;
Whether the individuals whose relationship with a child falls to be considered under Adoption and Children Act 2002, s 1(4)(f) is limited to blood relatives or should include the prospective adopters;
Whether it is necessary for a judge expressly to undertake an evaluation in the context of the Human Rights Act l998 in such circumstances and, if so, which rights are engaged.
The court said this about the 'nothing else will do' test at paragraph 68 of their judgment:
Since the phrase "nothing else will do" was first coined in the context of public law orders for the protection of children by the Supreme Court in Re B, judges in both the High Court and Court of Appeal have cautioned professionals and courts to ensure that the phrase is applied so that it is tied to the welfare of the child as described by Baroness Hale in paragraph 215 of her judgment:
"We all agree that an order compulsorily severing the ties between a child and her parents can only be made if "justified by an overriding requirement pertaining to the child's best interests". In other words, the test is one of necessity. Nothing else will do."
The phrase is meaningless, and potentially dangerous, if it is applied as some freestanding, shortcut test divorced from, or even in place of, an overall evaluation of the child's welfare. Used properly, as Baroness Hale explained, the phrase "nothing else will do" is no more, nor no less, than a useful distillation of the proportionality and necessity test as embodied in the ECHR and reflected in the need to afford paramount consideration to the welfare of the child throughout her lifetime (ACA 2002 s 1). The phrase "nothing else will do" is not some sort of hyperlink providing a direct route to the outcome of a case so as to bypass the need to undertake a full, comprehensive welfare evaluation of all of the relevant pros and cons (see Re B-S [2013] EWCA Civ 1146, Re R [2014] EWCA Civ 715 and other cases).
The court was clear that there is NO 'presumption' or 'right' for a child to be brought up by natural family and that those assessing the case had wrongly believed there was – thus the focus on the impact of removing A from the only parents she had known for 2 years, was not properly considered.
The issue is only and always the child's welfare. The matter was returned to another judge for a re-hearing. It will be interesting to know the outcome.
Time Limits for Appeals.
For more detailed discussion of the rules that apply to time limits, see this post about appealing against a care order. It is very important that you tell the court that you want to appeal and why you want to appeal within 21 days of the decision you want to challenge.
The court have considered appeals out of time in the case of re H (Children) [2015] and emphasised how important it is to stick to time limits in children cases. Although the father in this case was allowed to appeal some 8 months after the first decision, the court emphasised that this was 'exceptional'. See paras 33 and 34 of the judgment:
33.As a matter of law, if no notice of appeal is lodged during the 21 days permitted for the filing of a notice, a local authority should be entitled to regard any final care order and order authorising placement for adoption as valid authority to proceed with the task of placing the child for adoption. If that process has subsequently to be put on hold in order to allow a late application for permission to appeal to be determined, the impact upon the welfare of the child (particularly where prospective adopters who have been chosen may be deterred from proceeding) is also too plain to contemplate.
34. The problem that I have described is a necessary difficulty that arises from our system which contemplates that, notwithstanding the expiry of the 21 day period for lodging a notice of appeal, the court may, where to do so is justified, permit an appeal to proceed out of time. There will thus inevitably be a period after a late application for permission to appeal where time is taken to process the application before it is determined. Whilst accepting the inevitability of this source of, in some cases, highly adverse impact on the welfare of a child, every effort should be made to avoid its occurrence. One strategy which would seek to avoid the problem would be for the judge in every case where a final care and placement for adoption order is made to spell out to the parties the need to file any notice of appeal within 21 days and for the resulting court order to record on its face that that information was given to the parties by the judge. Secondly, this court and any appellate judge in the Family Court, must continue to strive to process any application for permission to appeal in a public law child case with the utmost efficiency. Finally, the fact that an application for permission to appeal which relates to a child in public law procedure is out of time should be regarded as a very significant matter when deciding whether to grant 'relief from sanctions' or an extension of time for appealing.
The adoption order has been made – can I challenge it?
This is very rare – but possible. However, those cases where adoption orders have been overturned appear to rest on procedural flaws in the application, not on the merits or otherwise of the adoption. The Websters for example, were denied the opportunity to challenge the adoption of their children on the basis that the children had lived apart from them for so long, it would not be in the children's interests to remove them from their adoptive homes.
The case of, ZH v HS & others [2019] EWHC 2190, gives a clear example of how mistakes made in how the adoption order was applied for and made, were so serious that they undermined the whole basis for the order and it was set aside.
T and her mother ZH tried to come to the UK from Somalia to claim asylum .T ended up with the maternal aunt and uncle who asked social workers to help them regularise T's status with them. They didn't get legal advice but went to a CAB and filled in the forms to make an application to adopt T, saying ZH was missing – as they didn't know where she was. T's mother then managed to enter the UK two years later. She was clearly out of time to appeal against the making of an adoption order so she applied under the court's inherent jurisidiction to set it aside. Every one agreed by the time this got to court in 2019, this was the right thing to do and T should be looked after by her mother.
The court was very critical about how the adoption order ever came to be made, calling the process 'flawed' and 'replete with errors and omissions', not least the correct notice wasn't given to the LA and there were no checks on the uncle and aunt and no guardian appointed for T.
It is indeed really worrying to think that such an application got through a court process without anyone apparently noticing such significant procedural failings and there is no surprise that the High Court found these errors were so serious they tainted the whole process; the adoption order could not stand.
However, we are waiting for the Court of Appeal's full judgment on another case where a mother has attempted to use the inherent jurisdiction to over turn an adoption order – her appeal was dismissed in June 2021 and it will be interesting to read the full reasons.
See further Julie Doughty's discussion at The Transparency Project, 'Can an adoption order be undone?'
I have taken the photograph above from this blog post – How does it feel to be adopted?
This entry was posted in I don't want my child to be adopted and tagged adoption, appeal, change of circumstances, placement order on June 6, 2015 by Sarah Phillimore.


It seems like everyone is writing about PSY these days. In the last 24 hours since I scheduled the posting I have seen 3 other blogs about it. PSY is really taking off!
This is absolutely nuts. I can't seem to ever keep up with the KPOP music here in Korea. There are quite a few groups, all with tons of people in them with names I can't seem to remember. All I do know is that some songs are incredibly catchy and are played all over the city.
Park Jae Sang, AKA PSY, has not had amazing luck with music. In the past, his music has been very controversial here in Korea. His first album got him fined and the second was banned!
Gangnam is a part of town in Seoul. There are a few things that I, living here, pick up on as hysterical (sunbathing in the park, the fun dance moves, swimming in the jinjilbang, the ajumas on the colorful party bus) If only I could figure out who that adorable boy is!
This song has over 10 million views on Youtube in 3 weeks and has been tweeted about by Josh Groban and T-Pain.
We had the best time in the noraebang with this on Saturday night. Two of our Korean co-workers put on an amazing show! Such a fun song, and I'm glad you've posted the translation.
Glad you posted this translation. We had the best time with our Korean co-workers and this song on Saturday night in the luxury noraebang!
No problem! Thanks for reading!!! It is such a catchy song.
:0) it is so catchy!


text:
L'équipe olympique grecque participe aux Jeux olympiques d'hiver de 1992 à Albertville. Elle n'y remporte aucune médaille. Le skieur Thomai Lefousi est le porte-drapeau d'une délégation grecque comptant 8 sportifs (7 hommes et 1 femme).

Résultats

Biathlon

Men

Ski alpin

Homme

Femme

Ski de fond

Men

Relais hommes 4 × 10 km

Voir aussi

Articles connexes

 Jeux olympiques d'hiver de 1992
 Grèce aux Jeux olympiques

Liens externes
  Grèce aux Jeux olympiques d'hiver de 1992 sur www.sports-reference.com
  Grèce sur le site du Comité international olympique
  Site officiel du comité olympique grec

1992
Grèce
Sport en Grèce en 1992

redpajama_set_name: RedPajamaWikipedia


LOS ANGELES (AP) — Authorities say a 15-year-old boy charged with stabbing his mother to death has been recaptured after escaping from a California juvenile facility.
More than 80 law enforcement personnel searched for Souzer after he escaped from Orange County Juvenile Hall just after midnight Friday. The teen got onto a roof and then jumped a perimeter fence.
Steve Sentman, chief probation officer for the county, said it's the first escape from the 434-bed facility in at least two decades.


text:
https://www.wsj.com/articles/deutsche-bank-poses-greatest-risk-to-global-financial-system-imf-says-1467239405
Deutsche Bank Poses Greatest Risk to Global Financial System, IMF Says
Fund says German bank most important contributor of systemic risks, followed by HSBC and Credit Suisse
The IMF said in its Financial Sector Assessment Program that Deutsche Bank's importance emphasizes the need for risk management, intense supervision and monitoring cross-border exposure.
Photo: Bloomberg News
By Hans Bentzien
Updated June 30, 2016 2:50 am ET
FRANKFURT—Deutsche Bank AG is the riskiest financial institution in the world as a potential source of external shocks to the financial system, according to the International Monetary Fund.
"Among the G-SIBs (globally systemically important banks), Deutsche Bank appears to be the most important net contributor to systemic risks, followed by HSBC and Credit Suisse," the IMF said in its Financial Sector Assessment Program.
The news came just after the Federal Reserve said U.S. units of Deutsche Bank and Spain's Banco Santander SA were the only ones among 33 banks that failed the final round of its "stress test," conducted to gauge how they would fare in a new financial crisis.
Fed Rejects Santander Holdings USA Inc's Capital Plan
Fed Stress Tests Clear 31 of 33 Big U.S. Banks
Finra Fines Deutsche Bank $6 Million Over Trading Data (June 29, 2016)
Deutsche Bank's Stress Test Lesson (June 23, 2016)
Deutsche Bank Unit Exceeds Fed Minimum Capital Level Under Stress Scenario (June 23, 2016)
Deutsche Bank's Asia-Pacific CEO to Leave (June 14, 2016)
The IMF also said the German banking system poses a higher degree of possible outward contagion compared with the risks it poses internally.
"In particular, Germany, France, the U.K. and the U.S. have the highest degree of outward spillovers as measured by the average percentage of capital loss of other banking systems due to banking sector shock in the source country," the IMF added.
The importance of Deutsche Bank emphasizes the need for risk management, intense supervision and monitoring cross-border exposure as well as the ability of globally systemic banks to carry out new resolution regimes, the IMF said.
A Deutsche Bank spokesman declined to comment on the IMF assessment.
Germany needs to examine whether its resolution plans for banks are operable, including a timely valuation of assets to be transferred, continued access to financial market infrastructures, and whether authorities can ensure control over a bank if resolution actions take a few days, if needed, by imposing a moratorium, the IMF said.
—Friedrich Geiger contributed to this article.
Write to Hans Bentzien at hans.bentzien@wsj.com


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The term "Machine Learning" is coined by Arthur Samuel in 1959 while at IBM.
1950 Alan Turing creates the "Turing Test" to determine if a computer has real intelligence. To pass the test, a computer must be able to fool a human into believing it is also human.
1950 Arthur Samuel wrote the first computer learning program. The program was the game of checkers, and the IBM computer improved at the game the more it played, studying which moves made up winning strategies and incorporating those moves into its program.
1967 The "nearest neighbor" algorithm was written, allowing computers to begin using very basic pattern recognition. This could be used to map a route for traveling salesmen, starting at a random city but ensuring they visit all cities during a short tour.
1979 Students at Stanford University invent the "Stanford Cart" which can navigate obstacles in a room on its own.
1981 Gerald Dejong introduces the concept of Explanation Based Learning (EBL), in which a computer analyses training data and creates a general rule it can follow by discarding unimportant data.
1985 Terry Sejnowski invents NetTalk, which learns to pronounce words the same way a baby does.
1997 IBM's Deep Blue beats the world champion at chess.
2006 Geoffrey Hinton coins the term "deep learning" to explain new algorithms that let computers "see" and distinguish objects and text in images and videos.
2011 IBM's Watson beats its human competitors at Jeopardy.
2012 Google's X Lab develops a machine learning algorithm that is able to autonomously browse YouTube videos to identify the videos that contain cats.
2014 Facebook FB develops DeepFace, a software algorithm that is able to recognize or verify individuals on photos to the same level as humans can.
2016 Google's artificial intelligence algorithm beats a professional player at the Chinese board game Go, which is considered the world's most complex board game and is many times harder than chess.
According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. He also suggested the term data science as a placeholder to call the overall field. You can refer to below, one the most famous venn diagram for Data Science.
How ML is different from AI ?
In the early days of AI, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. By 1980, expert systems had come to dominate AI, and statistics was out of favor.
Here is another famous venn diagram.
Hal Varian, Google's chief economist, predicted in 2008 that the job of statistician will become the "sexiest" around. Data, he explains, are widely available; what is scarce is the ability to extract wisdom from them. Data are becoming the new raw material of business: an economic input almost on a par with capital and labour.
Microsoft Excel is words most widely used data analytics tool. Its most widely used even before widespread usage of term data analytics.
type =GetURL in any cell and select cell having hyperlink, it will fetch only hyperlink.
TensorFlow is very easy to implement. Let us look how to get started with TensorFlow.
Google cloud and varied services provided on top of it are very confusing to comprehend. It's not just with google but you can visit AWS and azure, it's very confusion, rather I would say, since each cloud provided is using their own nomenclature for their services, it becomes difficult to remember what does what. Of course once you start using it, it will become natural to you but when you are getting started, this is frustrating. I have just completed a course How Google does Machine Learning so I thought let me make very simple note for myself and for other to keep track of it.
There are many services which are offered by Google but for beginners everything is not needed to get started, so I will split this post in two parts.
Google Cloud Platform is a suite of cloud computing services that runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search and YouTube.
Compute Engine – Infrastructure as a Service to run Microsoft Windows and Linux virtual machines.
App Engine – Platform as a Service to deploy Java, PHP, Node.js, Python, C#, .Net, Ruby and Go applications.
Cloud Storage – Object storage with integrated edge caching to store unstructured data.
Cloud Datalab – Tool for data exploration, analysis, visualization and machine learning. This is fully managed Jupyter Notebook service.
BigQuery – Scalable, managed enterprise data warehouse for analytics.
Cloud Natural Language – Text analysis service based on Google Deep Learning models.
Cloud Speech-to-Text – Speech to text conversion service based on machine learning.
Cloud Text-to-Speech – Text to speech conversion service based on machine learning.


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