UCLA

This paper serves as an investigation in the use of energy-based models for adversarial defense via purification and training. Convergent and non-convergent energy-based models are tasked to remove white-box adversarial signals embedded into images from the CIFAR-10 dataset so that they may be classified correctly. This work presents an analysis behind the stochastic behavior of MCMC sampling for adversarial noise reduction in meta-stable energy basins and the benefits and challenges associated with different regimes of energy-based learning for this task.