PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

Understand

Adversarial perturbations of normal images are usually imperceptible to humans, but they can seriously confuse state-of-the-art machine learning models.

• What makes them so special in the eyes of image classifiers? In this paper, we show empirically that adversarial examples mainly lie in the low probability regions of the training distribution, regardless of attack types and targeted models.
• Using statistical hypothesis testing, we find that modern neural density models are surprisingly good at detecting imperceptible image perturbations.
• Based on this discovery, we devised PixelDefend, a new approach that purifies a maliciously perturbed image by moving it back towards the distribution seen in the training data.