Title: Defending Against Adversarial Attacks by Leveraging an Entire GAN,
Abstract: Recent work has shown that state-of-the-art models are highly vulnerable to
adversarial perturbations of the input. We propose cowboy, an approach to
detecting and defending against adversarial attacks by using both the
discriminator and generator of a GAN trained on the same dataset. We show that
the discriminator consistently scores the adversarial samples lower than the
real samples across multiple attacks and datasets. We provide empirical
evidence that adversarial samples lie outside of the data manifold learned by
the GAN. Based on this, we propose a cleaning method which uses both the
discriminator and generator of the GAN to project the samples back onto the
data manifold. This cleaning procedure is independent of the classifier and
type of attack and thus can be deployed in existing systems.