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

Abstract

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 https://github.com/point0bar1/ebm-defense.

arXiv commentICLR 2021

References in corpus (10)

• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
• Wide Residual Networks
• Theoretically Principled Trade-off between Robustness and Accuracy
• Certified Adversarial Robustness via Randomized Smoothing
• Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
• Improving Generalization Performance by Switching from Adam to SGD
• Are Labels Required for Improving Adversarial Robustness?
• A Theory of Generative ConvNet
• Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets

Cited by in corpus (2)

• Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
• No MCMC for me: Amortized sampling for fast and stable training of energy-based models