Generative Cleaning Networks with Quantized Nonlinear Transform for Deep Neural Network Defense

Jianhe Yuan, Zhihai He

TL;DR: Defense against adversarial attacks.

Abstract:

Effective defense of deep neural networks against adversarial attacks remains a challenging problem, especially under white-box attacks.
In this paper, we develop a new generative cleaning network with quantized nonlinear transform for effective defense of deep neural networks. The generative cleaning network, equipped with a trainable quantized nonlinear transform block, is able to destroy the sophisticated noise pattern of adversarial attacks and recover the original image content. The generative cleaning network and attack detector network are jointly trained using adversarial learning to minimize both perceptual loss and adversarial loss. Our extensive experimental results demonstrate that our approach outperforms the state-of-art methods by large margins in both white-box and black-box attacks. For example, it improves the classification accuracy for white-box attacks upon the second best method by more than 40\% on the SVHN dataset and more than 20\% on the challenging CIFAR-10 dataset.

Keywords: Adversarial Defense, Adversarial Attack