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.
This paper addresses the issue of malicious use of generative models to fool authentication/anomaly detection systems that rely on sensor data. The authors formulate the scenario as a maxmin game between an authenticator and an attacker, with limitations on the number of samples available to the authenticator to fix a decision rule, the number of samples required at test time for the authenticator to take a decision and the number of leaked samples the attacker has access to. The authors prove that the game admits a Nash equilibrium and derive a closed form solution for the case of multivariate Gaussian data. Finally, the authors propose an algorithm called *GAN In the Middle* and perform experiments to show consistency with the theoretical results, better authentication performance than state of the art methods and usability for data augmentation. This pager should be accepted. Overall, it addresses crucial problems with the recent advances of generative models and provides significant theoretical results. The experiments would benefit from some clarification. For the experiments, the following should be addressed: * Confidence intervals for all results (specifically in Figure 1a and in Table 1) * Figure 1a: it would be interesting to see a similar analysis also for other values of m and k. * Table 1: This result would also be more supporting if experiments were performed for varying values of m, n and k. The description of the RS attack could be made more precise: does it mean that the attacker samples images at random? Intuitively, it feels confusing that the GIM authenticator would perform worse on this setting. * The experiments on handwritten data would be more similar to what I imagine being a real-world authentication scenario if performed on a task where a class is a single person writing multiple characters, as opposed to characters being classes. Minor comments: * Page 6, second to last row: there is a*the* repeated twice.
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>



This paper addresses the issue of malicious use of generative models to fool authentication/anomaly detection systems that rely on sensor data. The authors formulate the scenario as a maxmin game between an authenticator and an attacker, with limitations on the number of samples available to the authenticator to fix a decision rule, the number of samples required at test time for the authenticator to take a decision and the number of leaked samples the attacker has access to. The authors prove that the game admits a Nash equilibrium and derive a closed form solution for the case of multivariate Gaussian data. Finally, the authors propose an algorithm called *GAN In the Middle* and perform experiments to show consistency with the theoretical results, better authentication performance than state of the art methods and usability for data augmentation. This pager should be accepted. Overall, it addresses crucial problems with the recent advances of generative models and provides significant theoretical results. The experiments would benefit from some clarification. For the experiments, the following should be addressed: * Confidence intervals for all results (specifically in Figure 1a and in Table 1) * Figure 1a: it would be interesting to see a similar analysis also for other values of m and k. * Table 1: This result would also be more supporting if experiments were performed for varying values of m, n and k. The description of the RS attack could be made more precise: does it mean that the attacker samples images at random? Intuitively, it feels confusing that the GIM authenticator would perform worse on this setting. * The experiments on handwritten data would be more similar to what I imagine being a real-world authentication scenario if performed on a task where a class is a single person writing multiple characters, as opposed to characters being classes. Minor comments: * Page 6, second to last row: there is a*the* repeated twice.
assistant
This paper addresses the issue of malicious use of generative models to fool authentication/anomaly detection systems that rely on sensor data. The authors formulate the scenario as a maxmin game between an authenticator and an attacker, with limitations on the number of samples available to the authenticator to fix a decision rule, the number of samples required at test time for the authenticator to take a decision and the number of leaked samples the attacker has access to. The authors prove that the game admits a Nash equilibrium and derive a closed form solution for the case of multivariate Gaussian data. Finally, the authors propose an algorithm called *GAN In the Middle* and perform experiments to show consistency with the theoretical results, better authentication performance than state of the art methods and usability for data augmentation. This pager should be accepted. Overall, it addresses crucial problems with the recent advances of generative models and provides significant theoretical results. The experiments would benefit from some clarification. For the experiments, the following should be addressed: * Confidence intervals for all results (specifically in Figure 1a and in Table 1) * Figure 1a: it would be interesting to see a similar analysis also for other values of m and k. * Table 1: This result would also be more supporting if experiments were performed for varying values of m, n and k. The description of the RS attack could be made more precise: does it mean that the attacker samples images at random? Intuitively, it feels confusing that the GIM authenticator would perform worse on this setting. * The experiments on handwritten data would be more similar to what I imagine being a real-world authentication scenario if performed on a task where a class is a single person writing multiple characters, as opposed to characters being classes. Minor comments: * Page 6, second to last row: there is a*the* repeated twice.