On Breaking Deep Generative Model-based Defenses and Beyond

Jul 12, 2020



Deep neural networks have been proven to be vulnerable to the so-called adversarial attacks. Recently there have been efforts to defend such attacks with deep generative models. These defenses often involve an inversion phase that they first seek the latent representation that best matches with the input, then use this representation for prediction. Such defenses are often difficult to attack due to the non-analytical gradients. In this work, we develop a new gradient approximation attack to break these defenses. The idea is to view the inversion phase as a dynamical system, through which we extract the gradient with respect to the input by tracing its recent trajectory. An amortized strategy is further developed to accelerate the attack. Experiments show that our attack outperforms state-of-the-art approaches (e.g Backward Pass Differential Approximation) with unprecedented low distortions. Additionally, our empirical results reveal a key defect of current deep generative model-based defenses that it may not realize the on-manifold conjecture expectedly.



About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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