text:
\section{Introduction}

Face swapping has become an emerging topic in computer vision and graphics. Indeed, many works~\cite{DFL, DeepFakes, faceswap-GAN} on automatic face swapping have been proposed in recent years. These efforts have circumvented the cumbersome and tedious manual face editing processes, hence expediting the advancement in face editing. At the same time, such enabling technology has sparked legitimate concerns, particularly on its potential for being misused and abused. The popularization of ``Deepfakes'' on the internet has further set off alarm bells among the general public and authorities, in view of the conceivable perilous implications. Accordingly, there is a dire need for countermeasures to be in place promptly, particularly innovations that can effectively detect videos that have been manipulated.

Working towards forgery detection, various groups have contributed datasets (\eg, FaceForensics++~\cite{FF++data}, Deep Fake Detection~\cite{google} and DFDC~\cite{DFDC}) comprising manipulated video footages. The availability of these datasets has undoubtedly provided essential avenues for research into forgery detection. Nonetheless, the aforementioned datasets suffer several drawbacks. Videos in these datasets are either of a small number, of low quality, or overly artificial. Understandably, these datasets are inadequate to train a good model for effective forgery detection in \textit{real-world} scenarios. This is particularly true when current advances in human face editing are able to produce extremely realistic videos, rendering forgery detection a highly challenging task. On another note, we observe high similarity between training and test videos, in terms of their distribution, in certain works \cite{celebdf,FF++data}. Their actual efficacy in detecting \textit{real-world} face forgery cases, which are much more variable and unpredictable, remains to be further elucidated.

We believe that forgery detection models can only be enhanced when trained with a dataset that is exhaustive enough to encompass as many potential real-world variations as possible. To this end, we propose a large-scale dataset named DeeperForensics-$1.0$ consisting of $60,000$ videos with a total of $17.6$ million frames for real-world face forgery detection. The main steps of our dataset construction are shown in Figure~\ref{fig:first_figure}. We set forth three yardsticks when constructing this dataset: 1) \textit{Quality.} The dataset shall contain videos more realistic and much closer to the distribution of real-world detection scenarios. (Section~\ref{datacollection} and \ref{DFVAE}) 2) \textit{Scale.} The dataset shall be made up of a large-scale video sets. (Section~\ref{scaleanddiversity}) 3) \textit{Diversity.} There shall be sufficient variations in the video footages (\eg, compression, blurry, transmission errors) to match those that may be encountered in the real world (Section~\ref{scaleanddiversity}).

The primary challenge in the preparation of this dataset is the lack of good-quality video footages. Specifically, most publicly available videos are shot under an unconstrained environment resulting in large variations, including but not limited to suboptimal illumination, large occlusion of the target faces, and extreme head poses. Importantly, the lack of official informed consents from the video subjects precludes the use of these videos, even for non-commercial purposes. On the other hand, while some videos of manipulated faces are deceptively real, a larger number remains easily distinguishable by human eyes. The latter is often caused by model negligence towards appearance variations or temporal differences, leading to preposterous and incongruous results.

We approach the aforementioned challenge from two perspectives.
1) Collecting fresh face data from $100$ individuals with informed consents (Section~\ref{datacollection}).
2) Devising a novel method, DeepFake Variational Auto-Encoder (DF-VAE), to enhance existing videos (Section~\ref{DFVAE}).
In addition, we introduce diversity into the video footages through deliberate addition of distortions and perturbations, simulating real-world scenarios. We collate the newly collected data and the DF-VAE-modified videos into the DeeperForensics-$1.0$ dataset, with the aim of further expanding it gradually over time. We benchmark five representative open-source forgery detection methods using our dataset as well as a hidden test set containing manipulated videos that achieve high deceptive ranking in user studies.

We summarize our contributions as follows: 1) We propose a new dataset, DeeperForensics-$1.0$ that is larger in scale than existing ones, of high quality and rich diversity. To improve its quality, we introduce a carefully designed data collection and a novel framework, DF-VAE, that effectively mitigate obvious fabricated effects of existing manipulated videos. DeeperForensics-$1.0$ dataset shall facilitate future research in forgery detection of human faces in real-world scenarios. 2) We benchmark results of existing representative forgery detection methods on our dataset, offering insights into the current status and future strategy in face forgery detection.

\begin{table*}
\small
\centering
\begin{tabular}{c|c|c|c|c|c|c|c}
\Xhline{1.2pt}
Dataset  & Total videos & \tabincell{c}{Ratio\\(real : fake)}  &
\tabincell{c}{Controlled\\Capture} &
\tabincell{c}{Consented\\Actors} &
\tabincell{c}{Perturbations\\(total number)} &
\tabincell{c}{Perturbations\\(mixture)} &
\tabincell{c}{New\\Method} \\
\Xhline{1.2pt}
UADFV \cite{UADFV}  & 98  &  1 : 1  & $\times$  & -- & -- & $\times$ & $\times$ \\
DeepFake-TIMIT \cite{DFTIMIT} &620& only fake & $\times$ &-- & --& $\times$ & $\times$ \\
Celeb-DF \cite{celebdf} & 1203  &  1 : 1.95 & $\times$ & --& -- & $\times$ & $\times$ \\
FaceForensics++ \cite{FF++data} & 5000 & 1 : 4 & $\times$ & -- & 2 & $\times$ & $\times$ \\
\tabincell{c}{Deep Fake Detection \cite{google} \\
(joins FaceForensics++)}
       & 3431  &  1 : 8.5 & $\times$ & 28  & -- & $\times$ & $\times$ \\
DFDC Preview Dataset \cite{DFDC} & 5214 &  1 : 3.6 & $\times$ & 66 &  3 & $\times$ & $\times$ \\
\Xhline{1.2pt}
\textbf{DeeperForensics-1.0 (Ours)}  & \textbf{60000} &  5 : 1 & \ding{52} & \textbf{100} &  \textbf{35} & \ding{52} & \ding{52} \\
\Xhline{1.2pt}
\end{tabular}
\caption{The most relevant datasets compared to our dataset. DeeperForensics-1.0 is an order of magnitude larger in scale than existing datasets \textit{w.r.t.} both real and fake parts. We build a professional indoor environment to better control the important attributes of the collected data. $100$ paid actors give consents to the use and manipulation of their faces by signing a formal agreement. We employ seven types of perturbations at five intensity levels, leading to $35$ perturbations in total. The video may be subjected to a mixture of more than one perturbation. In contrast to prior works, we also introduce a new end-to-end high-fidelity face swapping method.}
 \vspace{-.05in}
\label{tab:datasetdiff}
\end{table*}

\section{Related Work}
This paper includes two main aspects of face forgery detection related to other works: dataset and benchmark. We will cover some important works in this section.

\noindent
\textbf{Face forgery detection datasets.} Building a dataset for forgery detection requires a huge amount of effort on data collection and manipulation. Early forgery detection datasets comprise images captured under highly restrictive conditions, \eg, MICC\_F2000 \cite{miccf2000}, Wild Web dataset \cite{wildweb}, Realistic Tampering dataset \cite{rtddataset}.

Owing to the urgency in video-based face forgery detection, some prominent groups have devoted their efforts to create face forensics video datasets (see Table \ref{tab:datasetdiff}). UADFV \cite{UADFV} contains $98$ videos, \ie, $49$ real videos from YouTube and $49$ fake ones generated by FakeAPP \cite{fakeapp}. DeepFake-TIMIT \cite{DFTIMIT} manually selects $16$ similar looking pairs of people from VidTIMIT \cite{VIDTIMIT} database. For each of the $32$ subjects, they generate about $10$ videos using low-quality and high-quality versions of faceswap-GAN \cite{faceswap-GAN}, resulting in a total of $620$ fake videos. Celeb-DF \cite{celebdf} includes $408$ YouTube videos, mostly of celebrities, from which $795$ fake videos are synthesized. FaceForensics++ \cite{FF++data} is the first large-scale face forensic dataset that consists of $4,000$ fake videos manipulated by four methods (\ie, DeepFakes \cite{DeepFakes}, Face2Face \cite{face2face}, FaceSwap \cite{FaceSwap}, NeuralTextures \cite{NeuralTextures})), and $1,000$ real videos from YouTube. Afterwards, Google joins FaceForensics++ and contributes Deep Fake Detection \cite{google} dataset with $3,431$ real and fake videos from $28$ actors. Recently, Facebook invites $66$ individuals and builds the DFDC preview dataset \cite{DFDC}, which includes $5,214$ original and tampered videos with three types of augmentations.

In comparison, we invite $100$ paid actors and collect high-resolution ($1920 \times 1080$) source data with various poses, expressions, and illuminations. 3DMM blendshapes \cite{3dmm} are taken as reference to supplement some extremely exaggerated expressions. We get consents from all the actors for using and manipulating their faces. In contrast to prior works, we also propose a new end-to-end face swapping method (\ie, DF-VAE) and systematically apply seven types of perturbations to the fake videos at five intensity levels. The mixture of distortions to a single video makes our dataset better imitate real-world scenarios. Ultimately, we construct DeeperForensics-$1.0$ dataset, which contains up to $60,000$ high-quality videos with a total of $17.6$ million frames.

\noindent
\textbf{Face forgery detection benchmarks.}
A new prominent benchmark, FaceForensics Benchmark \cite{FF++data}, for facial manipulation detection has been proposed recently. The benchmark includes six image-level face forgery detection baselines \cite{mosonet, FF++bm3, xception, FF++bm2, FF++bm1, FF++bm4}. Although FaceForensics Benchmark adds distortions to the videos by converting them into different compression rates, a deeper exploration of more perturbation types and their mixture is missing. Celeb-DF \cite{celebdf} also provides a face forgery detection benchmark including seven methods \cite{mosonet, xception, cdfbm2, cdfbm3, cdfbm4, UADFV, cdfbm1} trained and tested on different datasets.
In aforementioned benchmarks, the test set usually shares a similar distribution with the training set. Such an assumption inherently introduces biases and renders these methods impractical for face forgery detection in real-world settings with much more diverse and unknown fake videos.

In our benchmark, we introduce a challenging hidden test set with manipulated videos that achieve high deceptive scores in user studies, to better simulate \textit{real-world} distribution. Various perturbations are analyzed to make our benchmark more comprehensive. In addition, we mainly exploit \textit{video-level} forgery detection baselines \cite{i3d,resnet,lstm,c3d,tsn}. Temporal information -- a significant cue for video forgery detection besides single-frame quality -- has been considered. We will elaborate our benchmark in Section~\ref{benchmark}.

\section{A New Large-Scale Face Forensics Dataset}
\label{dataset}
The main contribution of this paper is a new large-scale dataset for real-world face forgery detection, DeeperForensics-$1.0$, which provides an alternative to existing databases. DeeperForensics-$1.0$ consists of $60,000$ videos with $17.6$ million frames in total, including $50,000$ original collected videos and $10,000$ manipulated videos. To construct a dataset more suitable for real-world face forgery detection, we design this dataset with careful consideration of \textit{quality}, \textit{scale}, and \textit{diversity}. In Section~\ref{datacollection} and~\ref{DFVAE}, we will discuss the details of data collection and methodology (\ie, DF-VAE) to improve \textit{quality}. In Section~\ref{scaleanddiversity}, we will show how to ensure large \textit{scale} and high \textit{diversity} of DeeperForensics-$1.0$.

\subsection{Data Collection}
\label{datacollection}

\begin{figure}
   \begin{center}

       \includegraphics[width=\linewidth]{figs_arxiv/data_collection_compare_v2.pdf}
   \end{center}
   \vspace{-0.45cm}
      \caption{Comparison of using only YouTube video and the collected video as source data, with the same method and setting.}
   \vspace{-0.35cm}
   \label{fig:data_collection_compare}
\end{figure}

\begin{figure*}
   \begin{center}

       \includegraphics[width=\linewidth]{figs_arxiv/data_collection_v3.png}
   \end{center}
   \vspace{-0.45cm}
      \caption{Diversity in identities, poses, expressions, and illuminations in our collected source data.}
   \vspace{-0.15cm}
   \label{fig:data_collection}
\end{figure*}

Source data is the first factor that highly affects \textit{quality}. Taking results in Figure~\ref{fig:data_collection_compare} as an example, the source data collection increases the robustness of our face swapping method to extreme poses, since videos on the internet usually have limited head pose variations.

We refer to the identity in the driving video as the ``target'' face and the identity of the face that is swapped onto the driving video as the ``source'' face. Different from previous works, we find that the source faces play a much more critical role than the target faces in building a high-quality dataset. Specifically, the expressions, poses, and lighting conditions of source faces should be much richer in order to perform robust face swapping. Hence, our data collection mainly focuses on source face videos. Figure~\ref{fig:data_collection} shows the diversity in different attributes of our data collection.

We invite $100$ paid actors to record the source videos. Similar to \cite{google, DFDC}, we obtain consents from all the actors for using and manipulating their faces to avoid the portrait right issues. The participants are carefully selected to ensure variability in genders, ages, skin colors, and nationalities. We maintain a roughly equal proportion \textit{w.r.t.} each of the attributes above. In particular, we invite $55$ males and $45$ females from $26$ countries. Their ages range from $20$ to $45$ years old to match the most common age group appearing on real-world videos. The actors have four typical skin tones: \textit{white}, \textit{black}, \textit{yellow}, \textit{brown}, with ratio $1$:$1$:$1$:$1$. All faces are clean without glasses or decorations.

\begin{figure}
   \begin{center}

       \includegraphics[width=\linewidth]{figs_arxiv/blendshape_v2.png}
   \end{center}
   \vspace{-0.35cm}
      \caption{Examples of 3DMM blendshapes in our data collection.}
   \vspace{-0.45cm}
   \label{fig:blendshape}
\end{figure}

Different from previous data collection in the wild (see Table~\ref{tab:datasetdiff}), we build a professional \textit{indoor} environment for a more controllable data collection. We only use the facial regions (detected and cropped by LAB \cite{wayne2018lab}) of the source data, so we can neglect the background. We set seven HD cameras from different angles: front, left, left-front, right, right-front, oblique-above, oblique-below. The resolution of our recorded videos is high ($1920 \times 1080$). We train the actors in advance to keep the collection process smooth. We request the actors to turn their heads and speak naturally with eight expressions: neutral, angry, happy, sad, surprise, contempt, disgust, fear. The head poses range from $-90^{\circ}$ to $+90^{\circ}$. Furthermore, the actors are asked to perform $53$ expressions defined in 3DMM blendshapes~\cite{3dmm} (see Figure~\ref{fig:blendshape}) to supplement some extremely exaggerated expressions. When performing 3DMM blendshapes, the actors also speak naturally to avoid excessive frames that show a closed mouth.

In addition to expressions and poses, we systematically set nine lighting conditions from various directions: uniform, left, top-left, bottom-left, right, top-right, bottom-right, top, bottom. The actors are only asked to turn their heads under uniform illumination, so the lighting remains unchanged on specific facial regions to avoid many duplicated data samples recorded by the cameras set at different angles.
In the end, our collected data contain over $50,000$ videos with a total of $12.6$ million frames -- an order of magnitude more than existing datasets.

\begin{figure}[t]
   \begin{center}

       \includegraphics[width=\linewidth]{figs_arxiv/style_mismatch_v3.png}
   \end{center}
   \vspace{-0.35cm}
      \caption{Examples of style mismatch problems in prominent face forensics datasets.}
   \vspace{-0.35cm}
   \label{fig:stype_mismatch}
\end{figure}

\begin{figure*}[t!]
   \begin{center}

       \includegraphics[width=\linewidth]{figs_arxiv/main_framework_v2.png}
   \end{center}
   \vspace{-0.35cm}
      \caption{The main framework of DeepFake Variational Auto-Encoder. In training, we reconstruct the source and target faces in blue and orange arrows, respectively, by extracting landmarks and constructing an unpaired sample as the condition. Optical flow differences are minimized after reconstruction to improve temporal continuity. In inference, we swap the latent codes and get the reenacted face in green arrows. Subsequent MAdaIN module fuses the reenacted face and the original background resulting in the swapped face.}
   \label{fig:main_framework}
   \vspace{-0.25cm}
\end{figure*}

\subsection{DeepFake Variational Auto-Encoder}
\label{DFVAE}

To tackle low visual \textit{quality} problems of previous works, we consider three key requirements in formulating a high-fidelity face swapping method:
1) It should be general and scalable for us to generate large number of videos with high quality.
2) The problem of face style mismatch caused by appearance variations need to be addressed. Some failure cases of existing methods are shown in Figure \ref{fig:stype_mismatch}.
3) Temporal continuity of generated videos should be taken into consideration.

Based on the aforementioned requirements, we propose DeepFake Variational Auto-Encoder (DF-VAE), a novel learning-based face swapping framework. DF-VAE consists of three main parts, namely a structure extraction module, a disentangled module, and a fusion module.
We will give a brief and intuitive understanding of the DF-VAE framework below. Please refer to the \textit{Appendix} for detailed derivations and results.

\begin{figure*}[t]
   \begin{center}

       \includegraphics[width=\linewidth]{figs_arxiv/m2m_v2.pdf}
   \end{center}
   \vspace{-0.35cm}
      \caption{Many-to-many (three-to-three) face swapping by a \textbf{single} model with obvious reduction of style mismatch problems. This figure shows the results between three source identities and three target identities. The whole process is end-to-end.}
   \label{fig:m2m}
   \vspace{-0.25cm}
\end{figure*}

\noindent
\textbf{Disentanglement of structure and appearance.}
The first step of our method is face reenactment -- animating the source face with similar expression as the target face, without any paired data. Face swapping is considered as a subsequent step of face reenactment that performs fusion between the reenacted face and the target background. For robust and scalable face reenactment, we should cleanly disentangle structure (\ie, expression and pose) and appearance representation (\ie, texture, skin color, \etc.) of a face. This disentanglement is rather difficult because structure and appearance representation are far from independent. We describe our solution as follows.

Let $\mathbf{x}_{1:T}\equiv{\{x_1,x_2,...,x_T\}}\in{X}$ be a sequence of source face video frames, and $\mathbf{y}_{1:T}\equiv{\{y_1,y_2,...,y_T\}}\in{Y}$ be the sequence of corresponding target face video frames. We first simplify our problem and only consider two specific snapshots at time $t$, $x_t$ and $y_t$. Let $\tilde{x}_t$, $\tilde{y}_t$, $d_t$ represent the reconstructed source face, the reconstructed target face, and the reenacted face, respectively.

Consider the reconstruction procedure of the source face $x_t$. Let $s_x$ denotes the structure representation and $a_x$ denotes the appearance information. The face generator can be depicted as the posteriori estimate $p_\theta\left(x_t|s_x,a_x\right)$. The solution of our reconstruction goal, marginal log-likelihood $\tilde{x}_t\sim\log{p_\theta\left(x_t\right)}$, by a common Variational Auto-Encoder (VAE) \cite{vae} can be written as:
\begin{equation}
\label{eq:1}
\begin{split}
    \log{p_\theta\left(x_t\right)}=D_{KL}\left(q_\phi\left(s_x,a_x|x_t\right)\|p_\theta\left(s_x,a_x|x_t\right)\right)\\
    +L\left(\theta,\phi;x_t\right),
\end{split}
\end{equation}
where $q_\phi$ is an approximate posterior to achieve the evidence lower bound (ELBO) in the intractable case, and the second RHS term $L\left(\theta,\phi;x_t\right)$ is the variational lower bound \textit{w.r.t.} both the variational parameters $\phi$ and generative parameters $\theta$.

In Eq.~\eqref{eq:1}, we assume that both $s_x$ and $a_x$ are latent priors computed by the same posterior $x_t$. However, the separation of these two variables in the latent space is rather difficult without additional conditions. Therefore, we employ a simple yet effective approach to disentangle these two variables.

The blue arrows in Figure~\ref{fig:main_framework} demonstrate the reconstruction procedure of the source face $x_t$. Instead of feeding a single source face $x_t$, we sample another source face $x^\prime$ to construct unpaired data in the source domain. To make the structure representation more evident, we use the stacked hourglass networks \cite{stackedhourglass} to extract landmarks of $x_t$ in the structure extraction module and get the heatmap $\hat{x}_t$. Then we feed the heatmap $\hat{x}_t$ to the Structure Encoder $E_\alpha$, and $x^\prime$ to the Appearance Encoder $E_\beta$. We concatenate the latent representations (small cubes in red and green) and feed it to the Decoder $D_\gamma$. Finally, we get the reconstructed face $\tilde{x}_t$, \ie, marginal log-likelihood of $x_t$.

Therefore, the latent structure representation $s_x$ in Eq.~\eqref{eq:1} becomes a more evident heatmap representation $\hat{x}_t$, which is introduced as a new condition. The unpaired sample $x^\prime$ with the same identity \textit{w.r.t.} $x_t$ is another condition, being a substitute for $a_x$. Eq.~\eqref{eq:1} can be rewritten as a conditional log-likelihood:
\begin{equation}
\label{eq:4}
\begin{split}
    \log{p_\theta\left(x_t|\hat{x}_t,x^\prime\right)}=D_{KL}\left(q_\phi\left(z_x|x_t,\hat{x}_t,x^\prime\right)\|p_\theta\left(z_x|x_t,\hat{x}_t,x^\prime\right)\right)\\
    +L\left(\theta,\phi;x_t,\hat{x}_t,x^\prime\right),
\end{split}
\end{equation}
The first RHS term KL-divergence is non-negative, we get:
\begin{equation}
\footnotesize
\label{eq:5}
\begin{split}
    &\log{p_\theta\left(x_t|\hat{x}_t,x^\prime\right)}\geq{L(\theta,\phi;x_t,\hat{x}_t,x^\prime)}\\
    &=\mathbb{E}_{q_\phi\left(z_x|x_t,\hat{x}_t,x^\prime\right)}\left[-\log{q_\phi\left(z_x|x_t,\hat{x}_t,x^\prime\right)}+\log{p_\theta\left(x_t,z_x|\hat{x}_t,x^\prime\right)}\right],
\end{split}
\end{equation}
and $L(\theta,\phi;x_t,\hat{x}_t,x^\prime)$ can also be written as:
\begin{equation}
\label{eq:6}
\begin{split}
    L\left(\theta,\phi;x_t,\hat{x}_t,x^\prime\right)=&-D_{KL}\left(q_\phi\left(z_x|x_t,\hat{x}_t,x^\prime\right)\|p_\theta\left(z_x|\hat{x}_t,x^\prime\right)\right)\\
    &+\mathbb{E}_{q_\phi\left(z_x|x_t,\hat{x}_t,x^\prime\right)}\left[\log{p_\theta\left(x_t|z_x,\hat{x}_t,x^\prime\right)}\right].
\end{split}
\end{equation}

We let the variational approximate posterior be a multivariate Gaussian with a diagonal covariance structure:
\begin{equation}
\label{eq:7}
\begin{split}
    \log{q_\phi\left(z_x|x_t,\hat{x}_t,x^\prime\right)}\equiv{\log{\mathcal{N}\left(z_x;\mathbf{\mu},\mathbf{\sigma^2}\mathbf{I}\right)}},
\end{split}
\end{equation}
where $\mathbf{I}$ is an identity matrix. Exploiting the reparameterization trick \cite{vae}, the non-differentiable operation of sampling can become differentiable by an auxiliary variable with independent marginal. In this case, $z_x\sim{q_\phi\left(z_x|x_t,\hat{x}_t,x^\prime\right)}$ is implemented by $z_x=\mu+\sigma\epsilon$ where $\epsilon$ is an auxiliary noise variable $\epsilon\sim\mathcal{N}(0,1)$. Finally, the approximate posterior $q_\phi(z_x|x_t,\hat{x}_t,x^\prime)$ is estimated by the separated encoders, Structure Encoder $E_\alpha$ and Appearance Encoder $E_\beta$, in an end-to-end training process by standard gradient descent.

We discuss the whole workflow of reconstructing the source face. In the target face domain, the reconstruction procedure is the same, as shown by orange arrows in Figure~\ref{fig:main_framework}.

During training, the network learns structure and appearance information in both the source and the target domains. It is noteworthy that even if both $y_t$ and $x^\prime$ belong to arbitrary identities, our effective disentangled module is capable of learning meaningful structure and appearance information of each identity.
During inference, we concatenate the appearance prior of $x^\prime$ and the structure prior of $y_t$ (small cubes in red and orange) in the latent space, and the reconstructed face $d_t$ shares the same structure with $y_t$ and keeps the appearance of $x^\prime$. Our framework allows concatenations of structure and appearance latent codes extracted from arbitrary identities in inference and permits \textit{many-to-many face reenactment}.

In summary, DF-VAE is a new conditional variational auto-encoder \cite{convae} with robustness and scalability. It conditions on two posteriors in different domains. In the disentangled module, the separated design of two encoders $E_\alpha$ and $E_\beta$, the explicit structure heatmap, and the unpaired data construction jointly force $E_\alpha$ to learn structure information and $E_\beta$ to learn appearance information.

\noindent
\textbf{Style matching and fusion.}
To fix the obvious style mismatch problems as shown in Figure \ref{fig:stype_mismatch}, we introduce a masked adaptive instance normalization (MAdaIN) module. We place a typical AdaIN \cite{adain} network after the reenacted face $d_t$. In the face swapping scenario, we only need to adjust the style of the face area and use the original background. Therefore, we use a mask $m_t$ to guide AdaIN \cite{adain} network to focus on style matching of the face area. To avoid boundary artifacts, we apply Gaussian Blur to $m_t$ and get the blurred mask $m_t^b$.

In our face swapping context, $d_t$ is the content input of MAdaIN, $y_t$ is the style input. MAdaIN adaptively computes the affine parameters from the face area of the style input:
\begin{equation}
\label{eq:8}
\begin{split}
    \rm{MAdaIN}\left(c, s\right)=\sigma\left(s\right)\left(\frac{c-\mu\left(c\right)}{\sigma\left(c\right)}\right)+\mu\left(s\right),
\end{split}
\end{equation}
where $c=m_t^b\cdot{d_t}$, $s=m_t^b\cdot{y_t}$. With the very low-cost MAdaIN module, we reconstruct $d_t$ again by Decoder $D_\delta$. The blurred mask $m_t^b$ is used again to fuse the reconstructed image with the background of $y_t$. At last, we get the swapped face $\overline{d}_t$. Figure~\ref{fig:style_matching} shows the effectiveness of MAdaIN module for style matching and fusion.

The MAdaIN module is jointly trained with the disentangled module in an end-to-end manner. Thus, by a \textit{single} model, DF-VAE can perform \textit{many-to-many face swapping} with obvious reduction of style mismatch and facial boundary artifacts (see Figure~\ref{fig:m2m} for the face swapping between three source identities and three target identities). Even if there are multiple identities in both the source domain and the target domain, the quality of face swapping does not degrade.

\begin{figure}
   \begin{center}

       \includegraphics[width=\linewidth]{figs_arxiv/style_matching_v2.pdf}
   \end{center}
   \vspace{-0.35cm}
      \caption{Comparison of the swapped face styles without or with MAdaIN module.}
   \vspace{-0.45cm}
   \label{fig:style_matching}
\end{figure}

\noindent
\textbf{Temporal consistency constraint.} Temporal discontinuity of fake videos leads to obvious flickering of the face area, making them very easy to be spotted by forgery detection methods and human eyes. To improve temporal continuity, we let the disentangled module to learn temporal information of both the source face and the target face.

For simplification, we make a Markov assumption that the generation of the frame at time $t$ sequentially depends on its previous $P$ frames $\mathbf{x}_{(t-p):(t-1)}$. In our experiment, we set $P=1$ to balance quality improvement and training time.

In order to build the relationship between a current frame and previous ones, we further make an intuitive assumption that the optical flows should remain unchanged after reconstruction. We use FlowNet 2.0 \cite{flownet2} to estimate the optical flow $\tilde{x}_f$ \textit{w.r.t.} $\tilde{x}_t$ and $x_{t-1}$, $x_f$ \textit{w.r.t.} $x_t$ and $x_{t-1}$. Since face swapping is sensitive to minor facial details which can be greatly affected by flow estimation, we do not warp $x_{t-1}$ by the estimated flow like \cite{vid2vid}. Instead, we minimize the difference between $\tilde{x}_f$ and $x_f$ to improve temporal continuity while keeping stable facial detail generation. To this end, we propose a new temporal consistency constraint, which can be written as:
\begin{equation}
\label{eq:11}
\begin{split}
    L_{temporal}=\frac{1}{CHW}\|\tilde{x}_f-x_f\|_1,
\end{split}
\end{equation}
where $C=2$ for a common form of optical flow.

We only discuss the temporal continuity \textit{w.r.t.} the source face in this section because the case of the target face is the same. If multiple identities exist in one domain, temporal information of all these identities can be learned in an end-to-end manner.

\subsection{Scale and Diversity}

\begin{table}
\centering
\resizebox{0.8\linewidth}{!}{
\begin{tabular}{c|c}
\Xhline{1.2pt}
No.   &  Distortion Type  \\
\Xhline{1.2pt}
1 & Color saturation change  \\
2  & Local block-wise distortion \\
3 &  Color contrast change \\
4  & Gaussian blur \\
5 & White Gaussian noise in color components \\
6  & JPEG compression \\
7  & Video compression rate change  \\
\Xhline{1.2pt}
\end{tabular}
}
\caption{Seven types of distortions in DeeperForensics-$1.0$.}
\label{tab:distortions}
\vspace{-0.1cm}
\end{table}

\label{scaleanddiversity}
Our extensive data collection and the proposed DF-VAE method are designed to improve the \textit{quality} of manipulated videos in DeeperForensics-$1.0$ dataset. In this section, we will mainly discuss the \textit{scale} and \textit{diversity} aspects.

We provide $10,000$ manipulated videos with $5$ million frames. It is also an order of magnitude more than the previous datasets.
We take $1,000$ refined YouTube videos collected by FaceForensics++~\cite{FF++data} as the target videos. Each face of our collected $100$ identities is swapped onto $10$ target videos, thus $1,000$ raw manipulated videos are generated directly by DF-VAE in an end-to-end process.
Thanks to the scalability and multimodality of DF-VAE, the time overhead of model training and data generation is reduced to $1/5$ compared to the common Deepfakes methods, with no degradation in quality. Thus, a larger-scale dataset construction is possible.

To ensure \textit{diversity}, we apply various perturbations to better simulate videos in real scenes. Specifically, as shown in Table~\ref{tab:distortions}, seven types of distortions defined in Image Quality Assessment (IQA)~\cite{kwanyee2018hallucinated,TID2013} are included. Each of these distortions is divided into five intensity levels. We apply random-type distortions to the $1,000$ raw manipulated videos at five different intensity levels, producing a total of $5,000$ manipulated videos. Besides, an additional of $1,000$ robust manipulated videos are generated by adding random-type, random-level distortions to the $1,000$ raw manipulated videos.
Moreover, in contrast to all the previous datasets, each sample of another $3,000$ manipulated videos in DeeperForensics-$1.0$ is subjected to a mixture of more than one distortion. The variability of perturbations improves the \textit{diversity} of DeeperForensics-$1.0$ to better imitate the data distribution of real-world scenarios.

DeeperForensics-$1.0$ is a new \textit{large-scale} dataset consisting of over $60,000$ videos with $17.6$ million frames for real-world face forgery detection. \textit{High-quality} source videos and manipulated videos constitute two main contributions of the dataset. The \textit{diversity} of perturbations applying to the manipulated videos ensures the robustness of DeeperForensics-$1.0$ to simulate real scenes. The whole dataset is released, free to all research communities, for developing face forgery detection and more general human-face-related research.

\subsection{User Study}
To examine the quality of DeeperForensics-$1.0$ dataset, we engage $100$ professional participants, most of whom specialize in computer vision research. We believe these participants are qualified and well-trained in assessing realness of tempered videos. The user study is conducted on DeeperForensics-$1.0$ and six former datasets, \ie, UADFV \cite{UADFV}, DeepFake-TIMIT \cite{DFTIMIT}, Celeb-DF \cite{celebdf}, FaceForensics++ \cite{FF++data}, Deep Fake Detection \cite{google}, DFDC \cite{DFDC}. We randomly select $30$ video clips from each of these datasets and prepare a platform for the participants to evaluate their realness. Similar to the user study of \cite{deepvideo}, the participants are asked to provide their feedbacks to the statement ``The video clip looks real.'' and give scores at five levels ($1$-clearly disagree, $2$-weakly disagree, $3$-borderline, $4$-weakly agree, $5$-clearly agree. We assume that users who give a score of $4$ or $5$ think the video is ``real''). The user study results are presented in Table~\ref{tab:userstudy}. The quality of our dataset is appreciated by most of the participants. Compared to the previous datasets, DeeperForensics-$1.0$ achieves the highest realism rating. Although Celeb-DF \cite{celebdf} also gets very high realness scores, the scale of our dataset is much larger.

\begin{table}
\centering
\resizebox{1.0\linewidth}{!}{
\begin{tabular}{c|ccccc|c}
\Xhline{1.2pt}
Dataset     &  1  &  2  &  3  &  4  &  5  &  ``real'' \\
\Xhline{1.2pt}
UADFV \cite{UADFV} & 29.2 & 36.0 & 20.7 & 8.9 & 5.2 & 14.1\% \\
DeepFake-TIMIT \cite{DFTIMIT} & 31.4 & 31.4 & 24.8 & 9.6 & 2.7 & 12.3\% \\
Celeb-DF \cite{celebdf}  & 5.6 & 14.8 & 18.6 & 24.2 & 36.9 & 61.0\% \\
FaceForensics++ \cite{FF++data}  & 46.8  & 31.4 & 13.4 & 4.4 & 4.0 & 8.4\% \\
Deep Fake Detection \cite{google}  & 26.0 & 28.0 & 24.1 & 11.5 & 10.3 & 21.9\% \\
DFDC \cite{DFDC} & 25.4 & 29.7 & 22.0 & 11.9 & 11.1 & 23.0\% \\
\Xhline{1.2pt}
\textbf{DeeperForensics-1.0 (Ours)} & 4.3 & 8.9 & 22.6 & 29.8 & 34.3 & \textbf{64.1\%} \\
\Xhline{1.2pt}
\end{tabular}
}
\caption{The percentage of user study ratings for UADFV, DeepFake-TIMIT, Celeb-DF, FaceForensics++, Deep Fake Detection, DFDC, and DeeperForensics-$1.0$ dataset. A higher score means the users think the videos are more realistic.}
\label{tab:userstudy}
\vspace{-0.2cm}
\end{table}

\section{Video Forgery Detection Benchmark}

\label{benchmark}
\noindent
\textbf{Dataset split.}
In our benchmark, we exploit $1,000$ raw manipulated videos in Section~\ref{scaleanddiversity} and $1,000$ YouTube videos from FaceForensics++ \cite{FF++data} as our \textit{standard} set. The videos are split into training, validation, and test set with a ratio of $7:1:2$. The identities of the swapped faces may be duplicated because faces of $100$ invited actors are swapped onto $1,000$ driving videos. To avoid data leak, we randomly choose unrepeated $70$, $10$, and $20$ identities, and group all the videos according to the identities. Similar to~\cite{FF++data}, the test and training sets share a close distribution in our \textit{standard} set.

Other experiments in our benchmark are conducted on different variants of the standard set. These variants share the same $1,000$ driving videos with the standard set. We will detail them in Section~\ref{resultsandanalysis}. For a fair comparison, all the experiments are conducted in the same split setting.

\noindent
\textbf{Hidden test set.}
For real-world scenarios, some experiments conducted in previous works~\cite{celebdf,FF++data} may not perform a convincing evaluation due to the huge biases caused by a close distribution between the training and the test sets. The aforementioned standard set has the same setting with these works. As a result, strong detection baselines obtain very high accuracy on the standard test set as demonstrated in Section~\ref{resultsandanalysis}. However, the ultimate goal of the face forensics dataset is to help detect forgery in real scenes. Even if the accuracy on the standard test set is high, the models may easily fail in real-world scenarios.

We argue that the test set of \textit{real-world} face forgery detection \textit{should not} share a close distribution with the training set. What we need is a test set that better simulates the real-world setting. We call it ``hidden'' test set. To better imitate fake videos in the real scene, the hidden test set should satisfy three factors:
1) \textit{Multiple sources.} Fake videos in-the-wild should be manipulated by different unknown methods.
2) \textit{High quality.} Threatening fake videos should have high quality to fool human eyes.
3) \textit{Diverse distortions.} Different perturbations should be taken into consideration.

Thus, in our initial benchmark, we introduce a challenging hidden test set with $400$ carefully selected videos. First, we collect fake videos generated by several unknown face swapping methods to ensure multiple sources. Then, we obscure all selected videos multiple times with diverse hidden distortions that are commonly seen in real scenes. Finally, we only select videos that can fool at least $50$ out of $100$ human observers in a user study. The ground truth labels are hidden and are used on our host server to evaluate the accuracy of detection models. Besides, the hidden test set will be enlarged constantly to get future versions along with development of Deepfakes technology. Fake videos manipulated by future face swapping methods will be included as long as they can pass the human test supported by us.

\subsection{Baselines}

Existing studies~\cite{celebdf,FF++data} primarily provide image-level face forgery detection benchmark. However, fake videos in-the-wild are much more menacing than manipulated images. We propose to conduct evaluation mainly based on video classification methods for two reasons. First, image-level face forgery detection methods do not consider any temporal information -- an important cue for video-based tasks. Second, image-level methods have been widely studied. We only choose one image-level method, XceptionNet \cite{xception}, which achieves the best performance in \cite{FF++data}, as one part of our benchmark for reference. The other four video-based baselines are C3D \cite{c3d}, TSN \cite{tsn}, I3D \cite{i3d}, and ResNet+LSTM \cite{resnet, lstm}, all of which have achieved promising results in video classification tasks. Details of all the baselines will be introduced in our \textit{Appendix}.

\subsection{Results and Analysis}
\label{resultsandanalysis}

Owing to the goal of detecting fakes in real-world scenarios, we mainly explore how common distortions appearing in real scenes affect the model performance. Accuracies of face forgery detection on the standard test set and the introduced hidden test set are evaluated under various settings.

\begin{table}
\centering
\resizebox{1.0\linewidth}{!}{
\begin{tabular}{c|c|c|c|c|c}
\Xhline{1.2pt}
Train &  FF++ DF  & FF++ F2F &    FF++ FS & FF++ NT & \textbf{DeeperForensics-1.0} \\
Test (acc) &   hidden   & hidden &  hidden & hidden & \textbf{hidden} \\
\Xhline{1.2pt}
C3D \cite{c3d} & 57.50   & 57.75  &  52.13  & 58.25  & \textbf{74.75}  \\
TSN \cite{tsn} & 57.63   & 57.25  &  53.50  & 57.38  & \textbf{77.00} \\
I3D \cite{i3d} & 56.63   & 58.38  &  54.63  & 63.63  & \textbf{79.25} \\
ResNet+LSTM \cite{resnet, lstm}  & 57.38 & 56.13 & 54.88 & 59.50 & \textbf{78.25} \\
XceptionNet \cite{xception} & 57.38 & 58.75 & 54.75 & 57.38 & \textbf{77.00} \\
\Xhline{1.2pt}
\end{tabular}
}
\caption{The binary detection accuracy of the baselines on the hidden test set when trained on four manipulated methods in FaceForensics++ (FF++): DeepFakes (DF), Face2Face (F2F), FaceSwap (FS), NeuralTextures (NT), and on DeeperForensics-$1.0$ standard training set without distortions.}
\label{tab:setting1}
\end{table}

\noindent
\textbf{Evaluation of effectiveness of DeeperForensics-1.0.} For a fair comparison, we evaluate DeeperForensics-$1.0$ and the state-of-the-art FaceForensics++ \cite{FF++data} dataset because they use the same driving videos. In this setting, we use $1,000$ raw manipulated videos without distortions in the standard set of DeeperForensics-$1.0$. For FaceForensics++, the same split is applied to its \textit{four} subsets. All the models are tested on the hidden test set (see Table~\ref{tab:setting1}).

The baselines trained on the standard training set of DeeperForensics-$1.0$ achieve much better performance on the hidden test set than all the \textit{four} subsets of FaceForensics++. This proves the higher \textit{quality} of DeeperForensics-$1.0$ over prior works, making it more useful for real-world face forgery detection.
In Table~\ref{tab:setting1}, I3D \cite{i3d} obtains the best performance on the hidden test set when trained on the standard training set. We conjecture that the temporal discontinuity of fake videos leads to higher accuracy by this video-level forgery detection method.

\begin{table}
\centering
\resizebox{1.0\linewidth}{!}{
\begin{tabular}{c|c|cc|cc|cc}
\Xhline{1.2pt}
Train       &  \textbf{std}   & std  & std & std/sing & std/rand & std/sing & std/rand \\
Test (acc)    & \textbf{std}  & std/sing & std/rand & std/sing & std/rand & std/rand & std/ sing\\
\Xhline{1.2pt}
C3D \cite{c3d} & \textbf{98.50} & 87.63 & 92.38 & 95.38 & 96.63 & 96.75 & 94.00 \\
TSN \cite{tsn} & \textbf{99.25} & 91.50 & 95.00 & 98.25 & 98.88 & 98.12 & 99.12 \\
I3D \cite{i3d} & \textbf{100.00} & 90.75 & 96.88 & 99.50 & 99.63 & 99.63 & 98.00 \\
ResNet+LSTM \cite{resnet, lstm} & \textbf{100.00} & 90.63 & 97.13 & 100.00& 98.63 & 100.00& 97.25 \\
XceptionNet \cite{xception} & \textbf{100.00} & 88.38 & 94.75 & 99.63 & 99.63 & 99.75 & 99.00 \\
\Xhline{1.2pt}
\end{tabular}
}
\caption{The binary detection accuracy of the baselines when trained and tested on DeeperForensics-$1.0$ dataset with different distortion perturbations. We analyze different training and testing settings on the standard set without distortions (std), the standard set with single-level distortions (std/sing), and the standard set with random-level distortions (std/rand).}
\label{tab:setting2}
\end{table}

\noindent
\textbf{Evaluation of dataset perturbations.} We study the effect of perturbations towards the forgery detection model performance. In contrast to prior work \cite{FF++data}, we try to evaluate the baseline accuracies when applying different distortions to the training and the test sets, in order to explore the function of perturbations in face forensics dataset.

In this setting, we conduct all the experiments on DeeperForensics-$1.0$ dataset with high diversity of perturbations. We use $1,000$ manipulated videos in the standard set (std), $1,000$ manipulated videos with single-level (level-5), random-type distortions (std/sing), $1,000$ manipulated videos with random-level, random-type distortions (std/rand). The data split is the same as that of the standard set with a ratio of $7:1:2$.

In Column $2$ of Table~\ref{tab:setting2}, we find the accuracy is nearly $100\%$ when the models are trained and tested on the standard set. This is reasonable because the strong baselines perform very well in a clean dataset with the same distribution.
In Columns $3$ and $4$, the accuracy decrease compared to Column $2$, when we choose std/sing and std/rand as the test set. Most of the video-level methods except C3D \cite{c3d} are more robust to perturbations on test set than XceptionNet \cite{xception}. This setting is very common because different distributions of the training and the test sets lead to decrease in model accuracies. Hence, the lack of perturbations in the face forensics dataset cutbacks the model performance for real-world face forgery detection with even more complex data distribution.

When we apply corresponding distortions to the training and test sets, the accuracy will increase (Column $5$ and $6$ in Table~\ref{tab:setting2}) compared to Column $3$ and $4$. However, this setting is impractical because the distributions of the training and test sets are still the same. We should augment the test set to better simulate the real-world distribution. Thus, some evaluation settings in previous works \cite{celebdf,FF++data} are unreasonable. If we swap the training set and the test set of std/sing and std/rand to further randomize the condition, results shown in Column $7$ and $8$ indicate that the accuracy remains high. This evaluation setting shows the possibility that with the same generation method, exerting appropriate distortions to the training set can make face forgery detection models more robust to real-world perturbations.

\begin{table}
\centering
\resizebox{0.95\linewidth}{!}{
\begin{tabular}{c|c|c|c|c}
\Xhline{1.2pt}
Train        & std  & std+std/sing & std+std/rand & \textbf{std+std/mix}  \\
Test (acc)     & hidden & hidden & hidden & \textbf{hidden}  \\
\Xhline{1.2pt}
C3D \cite{c3d}  & 74.75  & 78.25  & 78.13 &  \textbf{78.88}   \\
TSN \cite{tsn}  & 77.00  & 78.75  & 79.50 & \textbf{79.50} \\
I3D \cite{i3d}  & 79.25  & 80.13  & 80.13 & \textbf{80.13} \\
ResNet+LSTM \cite{resnet, lstm}  & 78.25 & 80.25 & 79.50 &  \textbf{80.25} \\
XceptionNet \cite{xception} & 77.00 & 79.75 & 79.75 &  \textbf{79.88} \\
\Xhline{1.2pt}
\end{tabular}
}

\caption{The binary detection accuracy of the baselines on the hidden test set when trained on DeeperForensics-$1.0$ dataset with the standard set without distortions (std), combination of std and the standard set with single-level distortions (std+std/sing), combination of std and the standard set with random-level distortions (std+std/rand), combination of std and the standard set with the mixed distortions(std+std/mix).}
\label{tab:setting3}
\end{table}

\noindent
\textbf{Evaluation of variants of training set for real-world face forgery detection.} We have conducted several experiments for evaluations of possible perturbations. Nevertheless, the case is more complex in real scenes because no information about the fake videos is available. The video may be subjected to more than one type and diverse levels of distortions. In addition to distortions, the method manipulating the faces is unknown.

From the evaluation of perturbations, we find the possibility of augmenting the training set to improve detection model performance. Thus, we further evaluate baseline performance on the hidden test set by devising some variants of the training set.
We perform experiments on DeeperForensics-$1.0$. In this setting, other than std, std/sing, and std/rand, we use additional $1,000$ manipulated videos, each of which is subjected to a mixture of three random-level, random-type distortions (std/mix). We combine std with std/sing, std/rand, and std/mix, respectively, yielding three new training sets (with the same data split as the former settings).

Column $2$ in Table~\ref{tab:setting3} shows the low accuracy when the models trained on std and tested on the hidden test set (same as Column $6$ in Table~\ref{tab:setting1}). Columns $3$ and $4$ indicate that the accuracy of all the baseline models increase when trained on std+std/sing and std+std/rand. The accuracy of I3D \cite{i3d} and ResNet+LSTM \cite{resnet, lstm}, are over $80\%$ in some cases. In a more complex setting, when the models are trained on std+std/mix, Column $5$ shows the accuracy of all the detection baselines further increase.

The results suggest that designing suitable training set variants has the potential to help increase face forgery detection accuracy, and applying various distortions to ensure the \textit{diversity} of DeeperForensics-$1.0$ is necessary. In addition, compared to image-level method, video-level face forgery detection methods have more potential capabilities to crack real-world fake videos as shown in Table~\ref{tab:setting3}.

Although the accuracy on the challenging hidden test set is still not very high, we provide two initial directions for future real-world face forgery detection research:
1) Improving the source data collection and generation method to ensure the \textit{quality} of the training set;
2) Augmenting the training set by various distortions to ensure its \textit{diversity}. We welcome researchers to make our benchmark more comprehensive.

\section{Discussion}

In this work, we propose a new large-scale dataset named DeeperForensics-$1.0$ to facilitate the research of face forgery detection towards \textit{real-world} scenarios. We make several efforts to ensure \textit{good quality}, \textit{large scale}, and \textit{high diversity} of this dataset. Based on the dataset, we further benchmark existing representative forgery detection methods, offering insights into the current status and future strategy in face forgery detection.
Several topics can be considered as future works.
1) We will continue to collect more source and target videos to further expand DeeperForensics.
2) We plan to invite interested researchers for contributing their video falsification methods to enlarge our hidden test set, as long as the fakes can pass the human test supported by us.
3) A better evaluation metric for face forgery detection methods is also an interesting research topic.
\\[5pt]
\noindent
\textbf{Acknowledgments.} This work is supported by the SenseTime-NTU Collaboration Project, Singapore MOE AcRF Tier 1 (2018-T1-002-056), NTU SUG, and NTU NAP. We gratefully acknowledge the exceptional help from Hao Zhu and Keqiang Sun for their contribution on source data collection and coordination.

{\small
\balance
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


text:
Rockers in Halloween Costumes: Alice in Chains, Metallica, Mastodon, Many More
Kirk Hammett, Tom Morello, Youth Code and others celebrated in style
See Teen Guitarist Mash Up System of a Down, Beethoven on 'America's Got Talent'
Polish musician shreds "Toxicity," "Symphony No. 5" on acoustic guitar
Daron Malakian Reveals Scars on Broadway Song That Nearly Went to SOAD
Band even rehearsed cut "once or twice, thinking of releasing it as a System song"
M. Shadows, Serj, Morello to Guest on System of a Down Drummer's Covers Album
John Dolmayan's These Grey Men project to finally release star-studded debut
Ghost, System of a Down, Foo Fighters to Play First-Ever Sonic Temple Festival
Ohio fest's stacked bill also features Bring Me the Horizon, Lamb of God, more
See System of a Down Unleash Fiery "I-E-A-I-A-I-O" Performance in 2003
Band broke out 'Steal This Album!' cut during hit-filled Reading Festival set
See Queen's "Bohemian Rhapsody" Covered in Style of System of a Down
10 Second Songs' Anthony Vincent channels Serj Tankian and Co. channeling Freddy Mercury and Co.
See System of a Down's Daron Malakian Discuss His Most Famous Riffs
He also recounts the tale of how he almost ended up a drummer instead
See System of a Down's Shavo Odadjian Talk Band's Rise, New Cannabis Line
Bassist remembers riot breaking out after cops shut down Hollywood show

redpajama_set_name: RedPajamaCommonCrawl


text:
89 schoolchildren in Johor infected with influenza
Students at a Chinese language school in Penang, where some students were also hit by flu, wearing face masks on Jan 9, 2020. Johor Baru has 56 cases which is the highest number in Johor state. PHOTO: BERNAMA
Jan 12, 2020, 9:34 pm SGT
https://str.sg/Jq7U
JOHOR BARU - A total of 89 schoolchildren in Johor have been infected with influenza.
Johor Education Department director Azman Adnan said on Sunday (Jan 12) that Johor Baru has 56 cases, which is the highest number in the state, reported Bernama news agency.
This is followed by the towns of Kulai (15), Tangkak (six), Kluang (five), Muar (five), and one in Batu Pahat and Pontian.
Mr Azman said data from government and private health centres showed that those affected include students from pre-school, primary school and secondary school.
"Schools are operating as usual. When there are one or two cases, the school's standard operating procedure applies, which means cleansing the classroom with disinfectant," he was quoted as saying by Bernama.
If the number of cases increases further, the health offices will decide whether to suspend classes or close the schools.
"So far, this has not been necessary in Johor," Mr Azman said, adding that the Johor Health Department had taken early measures to tackle the problem and is in constant contact with the health offices.
The schools also have instruments to detect students who have fever and they have been advised to clean the classrooms with disinfectant, he said.
Anyone with symptoms of influenza is not allowed to go to school. "They will be required to stay at home and seek treatment at the nearest clinic or hospital," he was quoted as saying.
Schools close, hospitals turn patients away as flu outbreak gets worse in Malaysia
Those found sick while in school will be quarantined in a special room or the sick bay.
Mr Azman said students staying in hostels will also be quarantined until they recover.


Rising against a backdrop of ultra-luxurious condominiums, III Cuscaden is the newest landmark in one of Singapore's most sought-after residential districts. A stone's throw from Orchard Road, III Cuscaden is a stunning new development tucked away in Cuscaden Walk. It offers an escape from the excitement of the city, yet with all the conveniences of Singapore's most famous shopping belt at your doorstep.
With its strategic location, you are only mere minutes away from Orchard Road shopping belt where a wide array of shopping and dining options await you.
The area where III Cuscaden is located is well represented by many elite schools which include River Valley Primary School, Anglo Chinese School, Raffles Girls' School, Crescent Girls' School, Singapore Chinese Girls' School and more.


text:
Jim and Jean Tyler
Known potter #35: Jim and Jean Tyler
David and I were very pleased to find this tall (28 cm high) sgraffito vase by Jim and Jean Tyler on eBay this month. We have acquired a number of pieces by this couple over the last few years, and find their work very appealing. In this example, there is a stong two-dimensional aspect to the decoration, which is only on one side, but the cylindrical form also works as a carved vessel. The drawing is a combination of geometric and organic forms, with some passages gouged quite strongly out of the clay.
Jim Tyler (1932-2001) was a teacher, potter, painter and architect based near Gosford, NSW. He studied architecture at Cape Town University, graduating in 1956,then practiced in Zimbabwe and London, before migrating to Australia with his wife Jean in 1959. They settled in Wollongong, then Wagga Wagga, where they both studied pottery in 1969 at the Wagga Wagga Technical College. During the 1970s they relocated to the Gosford area and set up a production pottery, producing functional ware and ceramic sculptures with the characteristic sgraffito decoration and soda ash vapour glaze seen in our vase, which also uses a pale grey slip as a canvas for the decoration.
The Tylers marked their work with a painted T shaped like the Greek letter Psi, accompanied by two dots.
From around 1977, Jim started to paint regularly and was engaged as artist in residence at Brewarrina NSW in 1987. In 2004, a retrospective exhibition of his work was held at Gosford Regional Gallery. The notes accompanying the exhibition are the main source for the information above. The couple also have entries in the 1977 and 1981 potters' directories.
Posted in Known potters and tagged Jim and Jean Tyler on March 29, 2010 by Judith. 1 Comment


text:
The wettest start to the summer for almost a century – University of Reading
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The wettest start to the summer for almost a century
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Release Date 02 July 2012
Nearly three times as much rain fell in Reading in June compared to the average for the month, according to meteorologists at the University of Reading.
The second wettest June in Reading in almost a century added to the miserable April and May weather to make the three-month period to June the rainiest late spring/early summer on record for 90 years.
A total of 123.2 mm of rain (corresponding to about 2.7 times the June average) fell at the University of Reading during June, making it the wettest June in the town since 1971, when 155 mm of rain was measured. In fact, in the past 90 years only June 1971 was wetter.
Roger Brugge, from the University of Reading's Department of Meteorology, said unusually for a wet June, persistent rain was to blame rather than thunderstorms.
Dr Brugge said: "Usually, one or two heavy thunderstorms are the reason for having a wet June. In 2012, however, no thunder was heard at the University while rain fell on 17 days with the longest rainless spell lasting just two days. This year much of the rain was brought by depressions and frontal systems - pushed towards southern Britain by the jet stream."
"June was slightly wetter than April - and the three-month period April to June 2012 brought 265.5 mm - the wettest such late spring/early summer in the past 90 years. Not surprisingly then, the perception of the start of this summer has justifiably been a poor one - with just a week of hot weather in late May seeming to break the wet spell.
"We saw a return to drier conditions during the final week of June, and fortunately in Reading we escaped the very heavy and damaging falls that affected some other parts of Britain."
The 10th-11th was the wettest two-day period during the month, giving a rainfall in 48 hours equivalent to the normal amount expected during the whole of June.
The same cloud that brought the rain has also been keeping the sunshine away. We had just 130 hours of sunshine in June - making the month duller than each of March, April and May this year. The June total was about two-thirds of the average June sunshine duration - making it the dullest June since 1991 with eight days failing to register one hour of sunshine.
The lack of sun also meant a cool month. June was the coldest since 2002 and only one day passed 25 degC. The month was 0.9 degC colder than average and ground frosts were measured on four mornings - the most in any June for seven years. Gardeners should note that in sheltered parts of Reading ground frosts can occur in any month of the year.
For more details, please contact Pete Castle at the University of Reading press office on 0118 378 7391 or p.castle@reading.ac.uk.
This note is based upon observations made at the University of Reading by the observer, Mike Stroud, and his colleagues.
While this June is the second wettest for 90 years, in earlier years June 1903 is recorded as the outstandingly wet June in the region. That year there was about 60 hours of near continuous rain from the 13th to the 15th in East Berkshire and the Thames flooded in many places as a result.
The University of Reading is ranked as one of the top 25 universities in the UK by both the Guardian and Times university guides and is listed among the top 1% of universities in the world (THE World University Rankings 2011-12). Reading is home to both the world-renowned Department of Meteorology and the Walker Institute for Climate System Research.
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text:
Killing A Woman to Kidnap Her Fetus Why This Is An Especially Appalling Murder
Killing A Woman to Kidnap Her Fetus: Why This Is An Especially Appalling Murder
By SHERRY F. COLB
Wednesday, Dec. 29, 2004
Two weeks ago, Kansas resident Lisa Montgomery reportedly cut open a pregnant woman's womb and stole her baby. The baby's mother died of her injuries, and Montgomery was arrested a few days later.
Charged with kidnapping resulting in death, the alleged perpetrator could face execution. The crime is shocking and cruel and leaves many feeling that capital punishment is as appropriate as it could ever be.
But other than the brutality of the crime, what accounts for the widely shared intuition that what Montgomery did constitutes an especially heinous act, worthy of the most severe penalty available?
The Background Facts: A Brutal Assault and Kidnapping
The name of the victim in this case was Bobbie Jo Stinnett. Stinnett lived in Kansas and was eight months pregnant when she met Montgomery. Though the two women were strangers, Montgomery had procured an invitation to her victim's home by feigning interest in a dog whom Stinnett had advertised on an Internet message board.
Once Montgomery arrived at the pregnant woman's home, she savagely attacked Stinnett, a month shy of her due date, cut her open, and kidnapped the baby girl from inside her. Stinnett's mother found her daughter lying in a pool of her own blood, barely breathing, her baby gone.
Unlike the unborn child of Laci Peterson, another pregnant murder victim whose case has lately been in the news, Bobbie Jo Stinnett's baby survived. In fact, the woman charged with murdering Stinnett intended for the baby to survive and become her own child. Shortly after the kidnapping, Montgomery was showing off the newborn as hers at a Kansas restaurant. Yet this fact does not seem to mitigate our perception of the severity of her crime. Indeed, it appears only to make it worse. Why?
Why Is Montgomery's Crime Worse?
One answer lies in Montgomery's motive for killing her victim.
A killer's reason for deciding to take another's life is extremely important in determining her level of culpability. Consider, for example, self-defense, a form of justifiable homicide. Such a killing is not a crime at all. The law has long recognized that each of us has the basic right to defend ourselves from being harmed by another person.
Other killers are considered blameworthy, but not quite as blameworthy as a murderer. For example, a person who kills in a state of legitimate rage caused by provocative behavior on the part of the victim of the killing, is -- in many jurisdictions -- guilty of manslaughter, rather than murder.
Though there are flaws in the manslaughter defense, it reflects the sense in which we understand a person's lashing out at someone who has provoked him. Though still wrongful and reprehensible, a killing in the course of reasonable extreme anger is a lesser species of evil than murder. That is because the victim has -- in some way -- acted to harm the killer first, and the self-protective impulse that animates self-defense law provides some mitigation for an otherwise inexcusable crime as well.
And finally, there are even moral gradations of murder.
What Makes Some Murders Worse Than Others?
Murder itself is wrong because the murderer decides to end the life of another person without that other person's having posed a threat to the murderer's life or even acted in a manner that might mitigate some of the blame. For no good reason, or for reasons having to do only with the murderer's own desires, she ends the life of another. Such an act denies the inherent worth of every life and uses another's life as a means to one's own objectives.
Now consider Montgomery's crime against Stinnett. The victim posed no threat to the perpetrator's life. Furthermore, Stinnett did nothing to Montgomery that would have justified rage. Indeed, Montgomery did not choose to kill Stinnett because of a personal grievance against her victim, or even a petty or foolish disagreement with her.
Instead, Montgomery chose Stinnett because she was pregnant with a child that Montgomery wanted to take. The killer killed, in other words, purely to gain access to the child who was living inside the victim's body.
Montgomery's actions thus represent the extreme of regarding another person as a mere means of serving one's own interests. Montgomery treated her victim as a vessel carrying loot that she could plunder. Not simply a means to the killer's own objectives, the victim here became a container. The killing was accordingly an absolute assault on Stinnett's personhood.
A Case that Provokes Comparable Outrage: Killing the Fetus, But Not the Mother
In criminal law, I teach Keeler v. Superior Court, a California Supreme Court case involving a man who beat his pregnant ex-wife's abdomen with the intention of killing the fetus inside her. This case, too, attracts a great deal of outrage when students read it each year. Though the woman in that case survived, and the child died, the assault on the victim's intrinsic dignity was quite similar to that committed against Bobbie Jo Stinnett.
In both attacks, the perpetrator's actions derived from a view of the woman as a living incubator. In Keeler, the ex-husband attacked the incubator to kill the baby inside, as though the mother's physical being were incidental and irrelevant.
In the Montgomery case, by contrast, the killer cut open the living incubator, killing her in the process, because she wanted to have the baby who rested inside. When the victim's mother came home to find her own daughter dying, the anguish of losing her beloved child could only have been compounded by the killer's apparent indifference to the independent existence and value of the woman whom she attacked.
A Woman's Life Is an End Unto Itself
As is reflected in debates about abortion, a pregnant woman is in a condition that distinguishes her from all other people -- men and women alike -- who are not pregnant. She holds a second life within her body.
That distinction places special burdens upon the pregnant woman's shoulders and renders her vulnerable to outside harms in a unique way. She cannot expose herself to danger without risking her baby's life at the same time. But to see her as a resource to be mined -- instead of as a precious being who holds within her a second precious being -- is to rob her of dignity.
Lisa Montgomery's crime is thus an outrage in several respects. In addition to killing a human being, and thereby also depriving a baby of her mother before the two had finished the journey of pregnancy together, the killer here treated her victim as a thing instead of a living creature. For that, she may well have forfeited her own entitlement to the mercy that a jury might otherwise have accorded her.
Sherry F. Colb, a FindLaw columnist, is a Professor at Rutgers Law School in Newark. Her earlier columns, including many on criminal law and procedure issues, may be found in the archive of her work on this site.


text:
Q: Integer solutions of $\log a \log b = \log c \log d$ Four positive integers $a,b,c,d>1$ satisfy $\log a \log b = \log c \log d$. Is necessarily $\frac{\log a}{\log c} \in \mathbb{Q}$ or $\frac{\log a}{\log d} \in \mathbb{Q}$?
I tried to use Gelfond–Schneider theorem and Lindemann–Weierstrass theorem from transcendence theory but it didn't work.

A: Yes, but I can only do it with something heavier, namely Schanuel's conjecture, or more precisely the following corollary of it.

Theorem (conditional on Schanuel's conjecture): Let $p_1, p_2, \dots$ be the primes. Then $\log p_1, \log p_2, \dots$ are algebraically independent over $\mathbb{Q}$.

Proof. By unique prime factorization, $\log p_1, \log p_2, \dots$ are linearly independent over $\mathbb{Q}$. Now apply Schanuel's conjecture with $z_i = \log p_i$ for all $n$. $\Box$
If $a$ is any positive integer, then expanding out $\log a$ using the prime factorization of $a$ allows us to write it as a homogeneous linear polynomial in the variables $\log p_i$ (with rational coefficients). Hence the identity
$$\log a \log b = \log c \log d$$
asserts that some homogeneous quadratic polynomial in the variables $\log p_i$ (with rational coefficients) has two factorizations into linear factors, which must necessarily agree up to a permutation and scalar multiplication because the ring $\mathbb{Q}[\log p_i]$, being a polynomial ring, is a UFD.

redpajama_set_name: RedPajamaStackExchange


text:
Q: OpenCV Haar Cascade xml format I'm interested in face detection and I'm trying to use OpenCV for my Delphi application. I'm going to use Google Picasa for facial recognition later. And I've found 4 different basic haar cascades for detecting frontal faces:
haarcascade_frontalface_default.xml
haarcascade_frontalface_alt.xml
haarcascade_frontalface_alt2.xml
haarcascade_frontalface_alt_tree.xml

The 'default' haarcascade gives too many false positives and is not good at all, while the 'alt', 'alt2' and 'tree' cascades seem to produce quite accurate, but sometimes different, results. The 'tree' cascade tends to produce more false negatives than the 'alt' and 'alt2' ones, but the least false positives as well.
The problem is that there're two different formats for these haarcascade xml files.
First like this: https://github.com/Danukeru/FOUCAM/blob/master/haarcascade_frontalface.xml
And second like this: https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_frontalface_alt.xml
If I'm not mistaken, the second one is considered the 'new' format, but my app currently understands only the first 'old' format. The 'new' cascades can probably be more accurate and I'd like to give them a try.
Is there any way to convert the new format cascades to an old format?
Or how else can I use them in my app?
Here's some code (from sites.google.com/site/josejp1/index/OCV.ZIP):
f1 := 'haarcascade_frontalface_alt.xml';
file1 := PChar(f1);
cascade_f := cvLoad(file1,0,0,0);
storage := cvCreateMemStorage(0);

s.width := 40;
s.height := 40;
faces := cvHaarDetectObjects(PCvArr(img), cascade_f, storage, 1.1, 3, 0, s);

And if I try to use the 'new' format xml cascade file I get an error:
OpenCV GUI Error Handler
---------------------------
Unspecified error (The node does not represent a user object (unknown type?))
in function cvRead, ..\..\cxcore\src\cxpersistence.cpp(5061)

I haven't found any ready-to-use examples in Delphi with new C++ API so I've converted this example of detectMultiScale (a C++ version of cvHaarDetectObjects) to Delphi. It compiles but crashes when tries to load a cascade. Im new to C++ so any help would be appreciated.
Delphi 2010 project in zip archive
unit Unit1;

interface

uses
  Windows,
  Messages,
  SysUtils,
  Variants,
  Classes,
  Graphics,
  Controls,
  Forms,
  Dialogs,
  StdCtrls,
  ExtCtrls,
  DateUtils,
  ExtDlgs,
  highgui_c,
  core_c,
  Core.types_c,
  imgproc_c,
  imgproc.types_c,
  objdetect;

type
  TForm1 = class(TForm)
    Image1: TImage;
    Button2: TButton;
    Label1: TLabel;
    Memo1: TMemo;
    procedure Button2Click(Sender: TObject);
  private
    { Private declarations }
  public
    { Public declarations }
  end;

var
  Form1: TForm1;
    mystorage: pCvMemStorage = nil;
    mycascade : pCvHaarClassifierCascade = nil;
    mycascade_name: AnsiString = 'cascades\haarcascade_frontalface_alt.xml';

implementation

{$R *.dfm}

procedure TForm1.Button2Click(Sender: TObject);

var img, grayImage : PIplImage;
    myobjects : TArray<TCvRect>;
    myscale : double;
    i: integer;
    r: pCvRect;
    mycolors : array[0..7] of TCvScalar;
    myCascadeClassifier : TCascadeClassifier;

begin

   mycolors[0] := CvScalar(0,0,255);
   mycolors[1] := CvScalar(0,128,255);
   mycolors[2] := CvScalar(0,255,255);
   mycolors[3] := CvScalar(0,255,0);
   mycolors[4] := CvScalar(255,128,0);
   mycolors[5] := CvScalar(255,255,0);
   mycolors[6] := CvScalar(255,0,0);
   mycolors[7] := CvScalar(255,0,255);

   img := cvLoadImage('lena.jpg');
   grayImage := cvCreateImage(cvGetSize(img),8,1);
   cvCvtColor(img,grayImage,CV_BGR2GRAY);
   cvShowImage('gray', grayImage);
   mystorage := cvCreateMemStorage(0);

   Memo1.Lines.Add('Memory allocated');

   mycascade := cvLoad('cascade.xml');
   myscale := 1.3;

   cvClearMemStorage(mystorage);
   myobjects := nil;
   myCascadeClassifier.detectMultiScale(grayImage,myobjects,1.1,3,CV_HAAR_SCALE_IMAGE or CV_HAAR_DO_CANNY_PRUNING,cvSize(0,0),cvSize(40,40));

   Memo1.Lines.Add('Object size? : ' + IntToStr(Length(myobjects)));

   for i := 0 to (Length(myobjects)-1) do
    begin
      cvRectangle(grayImage,cvPoint(r.x,r.y),cvPoint(r.x+r.width,r.y+r.height),CvScalar(0,0,255));
    end;

   cvNamedWindow('Output');
   cvShowImage('Output', grayImage);

   cvReleaseImage(grayImage);
   cvReleaseImage(img);

end;

end.

@TLama: I'm using the newest version of Delphi-OpenCV for this project with detectMultiScale, and some outdated (but working) version for the first project with cvHaarDetectObjects mentioned in the first post (sites.google.com/site/josejp1/index/OCV.ZIP).

A: if those delphi bindings are using the outdated c-api, (cvHaarDetectObjects) - you can only use the old format.
the c++ api ( cv::CascadeClassifier ) supports both, as well as hog and lbp cascades.
again, it's a limitation of the old c-api. if you can, avoid it !


Arnold and Helen are South African Nationals, looking to buy their first home in Hackney, London.
Arnold had inconsistent income – a normal basic income but bonus paid at various times of the year in various amounts and straight into his bank account. Arnold and Helen also wanted a product where they could overpay as much as they wanted whenever they wanted. Arnold is receiving a 100k annual bonus and plans to repay the mortgage. The couple also preferred the most flexible options. As they are first time buyers and new to the market, they didn't want to tie themselves to a rate or property for too long in case anything changed.
The lender I chose had a very competitive 2-year tracker rate allowing unlimited overpayments and no charges should you change your rate. They also use 100% of bonus income resulting in the perfect solution for the couple.


Acceptable Fold enrichment threshold fro ChIP?
I have recently performed some ChIP experiments and did qPCR for GAPDH promoter to check the specificity of the ChIP in RNA pol compared to IgG sample.
qPCR showed 2 fold enrichment of GAPDH promoter in RNA pol sample compared to IgG sample.
My specific question is; if 2 fold enrichment is acceptable or not? what is the threshold of that to consider a ChIP experiment specific enough to go for further steps of library prep and sequencing?
The specific TF which I am trying to do ChIP on, has no known target genes. So, there is no option for me to do qPCR on positive or negative binding regions of that gene to check the specificity.
Can anyone help me in what is considered as the threshold for fold enrichment?


The six 2015 DADIU Graduation Games will soon be ready. The deadline is next Friday, December 4th, where the teams will upload their six games to Google Play. The games will be presented to an invited audience on December 10th.
The Graduation Games are produced in November and December as the last part of the DADIU 2015 Semester. The Productions have taken place at Aalborg University in Nordkraft, at the IT University in Copenhagen, and at the Design School.
Below is a sneak peek from the six games.


On this professionally accredited programme, you will explore the latest counselling techniques, and undertake work placements.
This Postgraduate Diploma has an excellent record of supporting students into relevant employment.
As a careers guidance professional, you will be working with young people, adults, parents, teachers, employers and training providers.
This course equips you with the knowledge to support clients who are navigating the confusing maze of pathways in employment and education.
This one-year full-time course is recognised by the profession throughout the UK, and is accredited by the Career Development Institute.
Please note the application deadline for this programme is 31 July 2019.
With an excellent employment rate for graduates, our postgraduate diploma combines academic study with practical professional training, including six weeks of work placement. Teaching staff on the programme are qualified and experienced career advisers and bring additional specialist expertise to the course.
You will acquire effective interpersonal communication skills for one-to-one career guidance, working with groups and via online media. You will learn and put into practice the latest counselling techniques, while developing the necessary research and teamwork skills to carry you into your own successful career.
The skills taught are transferable to many other roles in employment, education and training.
Former students have gone on to work in education-industry liaison, and projects promoting mentoring or equality and diversity.
You will learn by a variety of teaching methods including lectures, tutorials, visits to employers, and independent study.
Career counselling skills are developed via individual coaching with an experienced careers adviser.
Facilities Based at our Sighthill Campus, students on the programme have access to a suite of interview training rooms equipped with CCTV facilities.
This module is intended to develop the skills, techniques and understanding necessary to deliver effective career guidance to individuals. You will be introduced to concepts from counselling and coaching psychology which underpin this module and their application in a career guidance context. The commonalities and differences between counselling and coaching will be explored.
A key element is learning how to develop and sustain constructive working relationships with individual clients and becoming skilled at supporting clients to identify and address their career development needs.
Your practical skills will be developed by background reading, observing experienced practitioners, using role-play, feedback on performance, discussion and reflecting on practice.
This module will assume no prior knowledge of psychology or sociology. Career theories will be compared and contrasted. The relationship between theory and practice will be explored, while placing guidance in its political context. Every opportunity will be taken to relate the value of theory to the practical day to day work of professional careers advisers. Psychometric testing and other assessment practices will be explored in relation to their underpinning theory.
This module is focused on designing and developing career-related learning experiences for pupils, students, and other service users. There are two main topics to the module. Firstly, there is a focus on the skills to design and deliver interactive group work. Secondly, a focus on learning programmes to deliver career education.
For the first topic you will be supported to develop the skills to design, lead and facilitate an active, participative career-related learning experience for a group. You will also develop skills for learning in groups.
You will build on this in the second topic by extending your learning beyond stand-alone events, and develop skills to design longer programmes of career learning. This will involve exploring the career education curriculum in educational institutions.
This learning will be underpinned by analysing theories and concepts of career learning.
This module will examine contemporary approaches to career guidance practice and the influence of underpinning theoretical concepts. You will explore new approaches to delivery of services, including use of new counselling techniques, assessment tools and guidance delivered at a distance (including e-mail, social media, interactive web services and telephone).
You will develop skills to begin to operate as an autonomous reflective practitioner, integrating information from diverse sources to refine your professional practice and incorporate the values and ethics of the profession into your work.
The module will give you a broad understanding of structures in education and employment, as well as government training and schemes to combat unemployment. You will be supported to undertake field research experience on visits to opportunity providers, which will give specific examples to balance the overview provided by formal teaching input. This culminates in an industry specific project which integrates the collection of LMI, making sense of the information and then presenting knowledge in accessible ways. This provides a holistic and experiential view of labour markets as they relate to the work of career practitioners.
In addition to University fees there are two other mandatory charges for this programme. Student membership and registration for the professional body qualification amounts to £365.00, which is payable to the Career Development Institute (CDI). All applicants must undergo a criminal record check via the Disclosure Scotland 'Protecting Vulnerable Groups' (PVG): The fee for this is £59.00 (reduced to £18.00 if you are already in the PVG scheme).


text:
Capitol Corridor Train 727
Latest status for Amtrak Capitol Corridor Train 727, updated 10:02 on 01/29 (unofficial).
Orig.: Sacramento, CA - Sacramento Valley StationOrigin: Sacramento, CA - Sacramento Valley Station, sch. departure 06:55 PT 01/29
Dest.: San Jose, CA - Diridon StationDestination: San Jose, CA - Diridon Station
SAC, departed 06:55 PT, on time (Sacramento - Sacramento Valley Station).
DAV, departed 07:10, on time, arrived 07:09 (Davis).
FFV, departed 07:30, on time, arrived 07:29 (Fairfield-Vacaville).
SUI, departed 07:36, on time, arrived 07:35 (Suisun-Fairfield).
MTZ, departed 08:04, 9 min. late, arrived 08:03 (Martinez).
RIC, departed 08:30, 9 min. late, arrived 08:29 (Richmond).
BKY, departed 08:37, 8 min. late, arrived 08:36 (Berkeley).
EMY, departed 08:42, 8 min. late, arrived 08:41 (Emeryville).
OKJ, departed 08:52, 7 min. late, arrived 08:49 (Oakland - Jack London Square Station).
OAC, departed 09:07, 12 min. late, arrived 09:06 (Oakland - Coliseum/Airport).
HAY, departed 09:17, 9 min. late, arrived 09:17 (Hayward).
FMT, departed 09:33, 10 min. late, arrived 09:32 (Fremont - Amtrak/ACE Station).
GAC, departed 09:48, 8 min. late, arrived 09:48 (Santa Clara - Great America).
SCC, departed 09:55, 7 min. late, arrived 09:55 (Santa Clara - Transit Center).
SJC, completed, 10:02 (San Jose - Diridon Station).
09:59 - 0 mi NW of San Jose [SJC], 37 mph SE
09:52 - 3 mi NW of Santa Clara [SCC], 58 mph SE
09:50 - 4 mi NW of Santa Clara [SCC], 3 mph SE
09:42 - 8 mi N of Santa Clara [GAC], 54 mph SE
09:37 - 9 mi N of Santa Clara [GAC], 62 mph SW
09:35 - 10 mi N of Santa Clara [GAC], 32 mph SW
09:27 - 1 mi NE of Fremont [FMT], 75 mph SE
09:22 - 7 mi NW of Fremont [FMT], 75 mph SE
09:20 - 8 mi NW of Fremont [FMT], 3 mph SE
09:12 - 6 mi NW of Hayward [HAY], 30 mph SE
09:08 - 8 mi NW of Hayward [HAY], 0 mph
09:04 - 0 mi NW of Oakland [OAC], 40 mph SE
08:57 - 3 mi NW of Oakland [OAC], 30 mph E
08:53 - 4 mi NW of Oakland [OAC], 0 mph
08:50 - 0 mi W of Oakland [OKJ], 26 mph SE
Twitter Feed: @CapitolCorridor
@CapitolCorridor
Last updated Sunday, 29-Jan-2023 12:11:09 PST


text:
Books about Esphyr Slobodkina
Rediscovering Slobodkina: A Pioneer of American Abstraction celebrates the extraordinarily prolific career of artist, illustrator, and author Esphyr Slobodkina.
By Sandra Kraskin, Karen Cantor, Leonard Marcus, and Ann Marie Mulhearn Sayer
184 pages, Hudson Hills Press, 2009
This comprehensive presentation brings new attention to the life and art of a pioneering woman. Esphyr Slobodkina's favorite illustrations and more than 90 of her paintings are paired with essays examining her work as an assemblage artist and sculptor, as well as her contributions to American Modernism as an abstract painter.
Sandra Kraskin summarizes Slobodkina's important contribution to American art in her catalog essay: Spanning most of the twentieth century, the narrative of her remarkable life-her independence, her varied talents, her unfailing commitment to abstract art -helps to create a broader perspective through which to view the development of abstract art in the United States. While her illustrations for the 1940 classic children's tale Caps for Sale brought her international renown-translated into thirteen languages, it has been in print for 68 years-Slobodkina was an abstract artist first. She lived in New York during the advent of Abstract Expressionism, and was one of the founding members of the American Abstract Artists, a group that included Josef Albers and Ad Reinhardt. The group was instrumental in bringing attention to the American Abstract art movement at a time when the primary focus was still on European artists. Throughout the 1940s and 1950s Slobodkina exhibited with Bryon Browne, John Graham, Adolph Gottlieb, and Mark Rothko. Slobodkina continued to produce and exhibit art until age 93.


MILWAUKEE (AP) — With every home run and big hit Allen Craig Jersey , it's getting more difficult for Christian Yelich to go out in Milwaukee without being recognized.It didn't stop the potential National League MVP from making a run to the supermarket for groceries, so his mother could make his favorite dish when she recently visited.Taco night must go on for the Milwaukee Brewers' All-Star outfielder and his family."So actually, he went to the market and bought everything. I sent him a list, so when I got there it was all ready to go and I just had to cook," said his mother, Alicia Yelich.She said the increased attention isn't going to her son's head, even with a late-season push for the Triple Crown that fell just short.The spotlight will shine brighter on Thursday when Yelich makes his postseason debut when the Brewers host the Colorado Rockies in Game 1 of their National League Division Series.The 26-year-old Yelich is known for his humility and steady demeanor, along with his talent at the plate. It's a lesson handed down in the family."I always say you've got to stay humble in life, right? The game is so hard that you really don't know how your night is going to be," Alicia Yelich said. "You can hit for the cycle or you get four (strikeouts). You've got to stay humble."Yelich has hit for the cycle twice this year, the first major leaguer to do so in one season against the same team, the Cincinnati Reds.Last week against Detroit http://www.padresfanproshop.com/authentic-tony-gwynn-jersey , Yelich made the first curtain call of his career after hitting his second home run to break a tie in the seventh inning of a 6-5 win.It was about as close as Yelich has come to showing emotion."First one ever, so I don't really know what to do," he said then.The loud fans at Miller Park chanted "MVP! MVP!" after the solo shot. He obliged with a brief appearance, lifting his helmet in the air about halfway up the dugout steps."Everyone was yelling at me to get up there, so I went up there really quick and went right back down," he said.Opponents are impressed by the demeanor, too. Rockies manager Bud Black got to talk to Yelich at the All-Star Game in July."You can't engage with everybody, but I found time to engage with him," Black said Wednesday. "And what a nice kid — I mean, really, really great to talk to, and really in a way humble and sort of shy http://www.padresfanproshop.com/authentic-dave-winfield-jersey , right?"That's sort of how I got it. But what a great kid. So, I'm very happy for him," Black said.Yelich hit an NL-best .326 to become the Brewers' first batting champion. He finished tied for third in home runs (36) and tied for second in RBIs (110).He's the favorite in the NL MVP race after batting .367 with 25 homers and 67 RBIs in 65 games after the All-Star break. His second-half surge is a big reason why the Brewers won the NL Central, making their first playoff appearance since 2011.Asked if success had changed her son, Alicia Yelich said, "Christian? No, no.""He's very even-keeled," she said. "And being even-keeled helps to be successful at this game." LOS ANGELES (AP) — Manny Machado caught his wife's first pitch before the game and hit a three-run homer to notch the first 100 RBI season of his career."It can't get any better than this," Machado said.Except maybe reaching the World Series and winning.The Dodgers' 7-2 victory over the San Diego Padres kept their NL West lead at 1 ½ games over Colorado, which won 5-1 at Arizona.Machado had been eager for a memorable moment since being traded to the Dodgers on July 18 from Baltimore.He borrowed a page from a popular script: homering on his bobblehead night."We fed off that," manager Dave Roberts said. "He's trying like heck and he wants that signature moment. Hopefully tonight alleviated some of the pressure for him." Machado's homer was one of three by the Dodgers in the game, giving them a National League-leading 222 on the season and breaking the club record of 221 set last year."We have great hitters here Rollie Fingers Jersey ," Machado said. "We're all confident. We know the team we have." Rich Hill became the Dodgers' first 10-game winner, allowing one run and four hits in seven innings. The left-hander struck out seven and walked none.Hill (10-5) has been stellar since the All-Star break with an 8-1 record. In his 12 second-half starts, the Dodgers are 9-3."I'm just excited we won and we're keeping pace here," Hill said.Machado caught a nice toss from his wife Yainee Alonso, whose brother is Indians first baseman Yonder Alonso."I think she's got the best (arm) in the family," Machado said.Then reminiscent of Manny Ramirez (2009) and Yasiel Puig (2015) homering on their bobblehead nights, Machado launched his 36th long ball of the season to right-center on the first pitch from rookie Jacob Nix (2-4). Machado had 24 homers with Baltimore before being traded on July 18.Fans responded with chants of "Manny! Manny!"The Dodgers batted around in the third, when Justin Turner doubled down the left-field line to drive in a pair of runs. Machado's shot scored Turner and Max Muncy, who was hit by Nix, giving the Dodgers a 6-0 lead. Yasmani Grandal hit his 24th homer off Brad Wieck in the sixth and Joc Pederson went deep in the first for his seventh leadoff homer of the season.San Diego's first run came on Hunter Renfroe's 25th homer with two outs in the sixth, leaving him one shy of tying his career high set last year.The Dodgers' bullpen struggled in the ninth.After getting a quick out, Yimi Garcia allowed a double to Austin Hedges. Tony Cingrani came on and hit Eric Hosmer before giving up a single to Wil Myers that loaded the bases. Manuel Margot followed with an RBI single.That forced Roberts to bring in closer Kenley Jansen http://www.padresfanproshop.com/authentic-randy-jones-jersey , who struck out the final two batters to earn his 37th save."I didn't expect to use him," Roberts said.Nix gave up a career-high-tying six runs and six hits in 2 1/3 innings. He struck out two and walked none."Made the mistake to Machado and the mistake to Cody Bellinger, and I paid for it," he said.LASORDA'S BIRTHDAYHall of Fame manager Tom Lasorda turned 91 on Saturday, and the sellout crowd of 53,536 cheered him when he was shown on the videoboard. He's in his record 69th season with the Dodgers, the longest tenure anyone has had with the franchise in Brooklyn and Los Angeles. Lasorda led the club to World Series titles in 1981 and '88.FAN ON FIELDA man ran onto the field from the third-base side in the middle of the eighth inning. He reached center field, where he was quickly tackled by about nine security personnel. They removed him through the center field gate.UP NEXTLHP Joey Lucchesi (8-8, 3.74 ERA) goes for the Padres in the series finale. The third straight rookie to start in the series, Lucchesi is 0-2 with a 5.00 ERA in two starts against LA. LHP Hyun-Jin Ryu (5-3, 2.18) starts the Dodgers' last home game before they end the regular season on the road. Ryu has given up three earned runs in 11 2/3 innings of two starts against the Padres.


2018 Heroes Gala. 6:30 pm. Reception, dinner, and program. Honoring HBO, Craig Newmark, Rob Riggle. Tickets from $500.00. Tables from $25,000.00. Classic Car Club Manhattan. New York. Contact: Marianne Hayes. Event address: 408 12th Ave., New York. Event web address: donate.iava.org/event/2018-heroes-gala/e174248.
10th Annual Heroes Gala. 6:30 pm. Cocktail reception, dinner, and program. Honoring Paul Rieckhoff. Tickets from $1,250.00. Cipriani 42nd Street. New York. (212) 982-9699. Event address: 110 East 42nd St., New York. Sponsored by Craigconnects. Event web address: www.iava.org/event/heroesgala-2015/.
Ninth Annual Heroes Gala. 6:00 pm. Dinner and awards. Hosted by Willie Geist. Honoring Daniel Rodriguez, Scott Wine. Tickets from $500.00. Cipriani 42nd Street. New York. (212) 219-0111. Event address: 110 East 42nd St., New York. Sponsored by American Heroes Channel, Craigconnects, DAV, Discovery Networks, HBO, Legg Mason Global Asset Management, Morgan Stanley, NBA Cares, TriWest Healthcare Alliance, Turner, USAA, Victory Motorcycles, WME. Noted attendees: Stephen Colbert. Event web address: www.iava.org/.
Tenth Anniversary Heroes Gala. 6:30 pm. Cocktail reception, dinner, and awards. Hosted by Willie Geist. Speaker(s): Paul Rieckhoff. Honoring Travis Kalanick, Admiral Mike Mullen. Tickets from $500.00. Cipriani 42nd Street. New York. Contact: Shannon Carey. (212) 982-9699. Event address: 110 East 42nd St., New York. Event web address: secure.iava.org/heroesgala-2014.
Seventh Annual Heroes Gala. 6:30 pm. Cocktail reception, dinner & award presentation. Business attire or equivalent military dress. Hosted by Brian Williams. Honoring Salvatore Giunta, Jon Stewart. Tickets from $500.00. Cipriani 42nd Street. New York. Contact: Kate Kelly. (212) 982-9699. Event address: 110 East 42nd St., New York. Sponsored by CNN, Miller High Life, Southwest Airlines, Western Asset. Event web address: secure.iava.org/heroesgala-2013.
2012 Annual Benefit. 6:30 pm. Cocktails and dinner. Cipriani 42nd Street. New York. Contact: Jessica Laufer. Event address: 110 East 42nd St., New York. Event web address: www.iava.org.
Fifth Annual Heroes Gala. 6:30 pm. Cocktails and dinner. MC, Brian Williams. Business attire. Honoring Stephen Colbert. Tickets from $500.00. Tables from $12,500.00. Cipriani 42nd Street. New York. Contact: Jessica Laufer . (212) 245-6570. Event address: 110 East 42nd St., New York. Event web address: www.iava.org.
Benefit Event. 6:30 pm. Cocktails, dinner and program. Gotham Hall. New York. Contact: Debbie Fife. (212) 245-6570. Event address: 1356 Broadway, New York.
Third Annual Heroes Gala. 6:00 pm. Cocktails and program. Tickets from $400.00. Gotham Hall. New York. Contact: Pam Geiger. (212) 581-1400. Event address: 1356 Broadway, New York. Fax: (212) 581-1442.
First Annual Fall Benefit. 6:00 pm. Cocktail reception, silent auction and awards. Honoring Tatiana Platt, Bobby Smith. Gotham Hall. New York. Contact: Kara Falkenstein. (212) 982-9699. Event address: 1356 Broadway, New York.


My hair is a cruel mistress. It smiles and plays nice in front of our friends, but behind closed doors it loves to belittle me. I could (should) make a whole post about the trials and tribulations of my locks, but that's probably best saved for a later time. All I'll say for now is that there are too many tutorials on the internet taunting girls like me with hair too fine to do much with.
Sometimes though, it shows me some love. Thanks for maintaining your curl last night, friend.