Optimal Strategies Against Generative Attacks
Generative neural models have improved dramatically recently. With this progress comes the risk that such models will be used to attack systems that rely on sensor data for authentication and anomaly detection. Many such learning systems are installed worldwide, protecting critical infrastructure or private data against malfunction and cyber attacks. We formulate the scenario of such an authentication system facing generative impersonation attacks, characterize it from a theoretical perspective and explore its practical implications. In particular, we ask fundamental theoretical questions in learning, statistics and information theory: How hard is it to detect a ``fake reality**? How much data does the attacker need to collect before it can reliably generate nominally-looking artificial data? Are there optimal strategies for the attacker or the authenticator? We cast the problem as a maximin game, characterize the optimal strategy for both attacker and authenticator in the general case, and provide the optimal strategies in closed form for the case of Gaussian source distributions. Our analysis reveals the structure of the optimal attack and the relative importance of data collection for both authenticator and attacker. Based on these insights we design practical learning approaches, and show that they result in models that are more robust to various attacks on real-world data. Code will be made publicly available upon publication.
*Optimal Strategies Against Generative Attacks* describes just what the title implies - various dimensions of the problem of defending against a generative adversary, with theoretical discussion under limited settings, as well as practical experiments extending on the intuitions gained using the theoretical exploration under limited conditions. Particularly, one of the key stated goals of the paper is to *construct a theorectical framework for studying the security risk arising from generative models, and explore its practical implications*. Given this goal, the paper performs admirably. The appendix is extensive, and gives a lot more insight into the core paper itself. I am unclear on how the data augmentation experiment fits into the overall picture - perhaps a more detailed explanation of how and why this would be used to form an *attack* would help. The other experiments are sensible, and demonstrate reasonable and expected results. This is a solid paper, and most of my critiques are *out of scope* and revolve around experiments that would be nice to see. Though GAN-for-text is not simple, showing this type of setup for text would be interesting for a number of reasons, same for audio. Continual passes through the text, with a focus on clarity could also be helpful - the topic is dense, and the text does a good job describing what is happening, but it is always possible to further distill these complex topics, and relegate some useful-but-not-critical pieces to the appendix. These are minor quibbles, and overall this paper was an interesting and useful read, on a relatively underexplored topic. It shows theorectical results, and practical experimental demonstrations of the theories proposed. Given the stated goals of the paper, it performs admirably.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Optimal Strategies Against Generative Attacks
Generative neural models have improved dramatically recently. With this progress comes the risk that such models will be used to attack systems that rely on sensor data for authentication and anomaly detection. Many such learning systems are installed worldwide, protecting critical infrastructure or private data against malfunction and cyber attacks. We formulate the scenario of such an authentication system facing generative impersonation attacks, characterize it from a theoretical perspective and explore its practical implications. In particular, we ask fundamental theoretical questions in learning, statistics and information theory: How hard is it to detect a ``fake reality**? How much data does the attacker need to collect before it can reliably generate nominally-looking artificial data? Are there optimal strategies for the attacker or the authenticator? We cast the problem as a maximin game, characterize the optimal strategy for both attacker and authenticator in the general case, and provide the optimal strategies in closed form for the case of Gaussian source distributions. Our analysis reveals the structure of the optimal attack and the relative importance of data collection for both authenticator and attacker. Based on these insights we design practical learning approaches, and show that they result in models that are more robust to various attacks on real-world data. Code will be made publicly available upon publication.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



*Optimal Strategies Against Generative Attacks* describes just what the title implies - various dimensions of the problem of defending against a generative adversary, with theoretical discussion under limited settings, as well as practical experiments extending on the intuitions gained using the theoretical exploration under limited conditions. Particularly, one of the key stated goals of the paper is to *construct a theorectical framework for studying the security risk arising from generative models, and explore its practical implications*. Given this goal, the paper performs admirably. The appendix is extensive, and gives a lot more insight into the core paper itself. I am unclear on how the data augmentation experiment fits into the overall picture - perhaps a more detailed explanation of how and why this would be used to form an *attack* would help. The other experiments are sensible, and demonstrate reasonable and expected results. This is a solid paper, and most of my critiques are *out of scope* and revolve around experiments that would be nice to see. Though GAN-for-text is not simple, showing this type of setup for text would be interesting for a number of reasons, same for audio. Continual passes through the text, with a focus on clarity could also be helpful - the topic is dense, and the text does a good job describing what is happening, but it is always possible to further distill these complex topics, and relegate some useful-but-not-critical pieces to the appendix. These are minor quibbles, and overall this paper was an interesting and useful read, on a relatively underexplored topic. It shows theorectical results, and practical experimental demonstrations of the theories proposed. Given the stated goals of the paper, it performs admirably.
assistant
*Optimal Strategies Against Generative Attacks* describes just what the title implies - various dimensions of the problem of defending against a generative adversary, with theoretical discussion under limited settings, as well as practical experiments extending on the intuitions gained using the theoretical exploration under limited conditions. Particularly, one of the key stated goals of the paper is to *construct a theorectical framework for studying the security risk arising from generative models, and explore its practical implications*. Given this goal, the paper performs admirably. The appendix is extensive, and gives a lot more insight into the core paper itself. I am unclear on how the data augmentation experiment fits into the overall picture - perhaps a more detailed explanation of how and why this would be used to form an *attack* would help. The other experiments are sensible, and demonstrate reasonable and expected results. This is a solid paper, and most of my critiques are *out of scope* and revolve around experiments that would be nice to see. Though GAN-for-text is not simple, showing this type of setup for text would be interesting for a number of reasons, same for audio. Continual passes through the text, with a focus on clarity could also be helpful - the topic is dense, and the text does a good job describing what is happening, but it is always possible to further distill these complex topics, and relegate some useful-but-not-critical pieces to the appendix. These are minor quibbles, and overall this paper was an interesting and useful read, on a relatively underexplored topic. It shows theorectical results, and practical experimental demonstrations of the theories proposed. Given the stated goals of the paper, it performs admirably.