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.

References in corpus (4)

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• BEGAN: Boundary Equilibrium Generative Adversarial Networks
• Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
• MagNet: a Two-Pronged Defense against Adversarial Examples

Cited by in corpus (7)

• Motivating the Rules of the Game for Adversarial Example Research
• Adversarial Examples - A Complete Characterisation of the Phenomenon
• Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions
• Stateful Detection of Model Extraction Attacks
• Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference
• FUNN: Flexible Unsupervised Neural Network
• Adversarial Data Encryption