Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models

By Mitch Hill, Jonathan Mitchell, Song-Chun Zhu. 2020

97 citations recorded by the source corpus.

The vulnerability of deep networks to adversarial attacks is a central problem for deep learning from the perspective of both cognition and security. The current most successful defense method is to train a classifier using adversarial images created during learning. Another defense approach involves transformation or purification of the original input to remove adversarial signals before the image is classified. We focus on defending naturally-trained classifiers using Markov Chain Monte Carlo (MCMC) sampling with an Energy-Based Model (EBM) for adversarial purification. In contrast to adversarial training, our approach is intended to secure pre-existing and highly vulnerable classifiers. The memoryless behavior of long-run MCMC sampling will eventually remove adversarial signals, while metastable behavior preserves consistent appearance of MCMC samples after many steps to allow accurate long-run prediction. Balancing these factors can lead to effective purification and robust classification. We evaluate adversarial defense with an EBM using the strongest known attacks against purification. Our contributions are 1) an improved method for training EBM's with realistic long-run MCMC samples, 2) an Expectation-Over-Transformation (EOT) defense that resolves theoretical ambiguities for stochastic defenses and from which the EOT attack naturally follows, and 3) state-of-the-art adversarial defense for naturally-trained classifiers and competitive defense compared to adversarially-trained classifiers on Cifar-10, SVHN, and Cifar-100. Code and pre-trained models are available at this https URL.

Subfields: Artificial Intelligence, Materials Chemistry

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Citations in the corpus

Energy-based Latent Aligner for Incremental Learning (2022, methodology): “EBMs have been used for out-of-distribution sample detection [30, 47], structured prediction [4, 5, 48] and improving adversarial robustness [11, 17].”
Diffusion Models for Adversarial Purification (2022, methodology): “Because most prior adversarial purification methods have an optimization or sampling loop in their defense process (Hill et al., 2021), they cannot be evaluated directly with the strongest white-box adaptive attacks, such as AutoAttack.”