Resilience of GANs against Adversarial Attacks

The goal of this paper is to explore the resilience of Generative Adversarial Networks(GANs) against adversarial attacks. Specifically, we evaluated the threat potential of an adversarial attack against the discriminator part of the system. Such an attack aims to distort the output by injecting maliciously modified input during training. The attack was empirically evaluated against four types of GANs, injections of 10% and 20% malicious data, and two datasets. The targets were CGAN, ACGAN, WGAN, and WGAN-GP. The datasets were MNIST and F-MNIST. The attack was created by improving an existing attack on GANs. The lower bound for the injection size turned out to be 10% for the improvement and 10-20% for the baseline attack. It was shown that the attack on WGAN-GP can overcome a filtering defence for F-MNIST.