A Learning and Masking Approach to Secure Learning

TL;DR

This paper introduces a new perspective on defending neural networks against adversarial attacks by framing attack generation as a learning problem and proposing masking techniques that enhance robustness against various attack types.

Contribution

It presents a novel dual approach to attack learning for defense and introduces a masking method that adds noise to logits to protect against low distortion attacks.

Findings

Effective against high perturbation attacks like FGSM

Protects against low distortion attacks such as CW

Works simultaneously with attack learning for comprehensive defense

Abstract

Deep Neural Networks (DNNs) have been shown to be vulnerable against adversarial examples, which are data points cleverly constructed to fool the classifier. Such attacks can be devastating in practice, especially as DNNs are being applied to ever increasing critical tasks like image recognition in autonomous driving. In this paper, we introduce a new perspective on the problem. We do so by first defining robustness of a classifier to adversarial exploitation. Next, we show that the problem of adversarial example generation can be posed as learning problem. We also categorize attacks in literature into high and low perturbation attacks; well-known attacks like fast-gradient sign method (FGSM) and our attack produce higher perturbation adversarial examples while the more potent but computationally inefficient Carlini-Wagner (CW) attack is low perturbation. Next, we show that the dual approach of the attack learning problem can be used as a defensive technique that is effective against high perturbation attacks. Finally, we show that a classifier masking method achieved by adding noise to the a neural network's logit output protects against low distortion attacks such as the CW attack. We also show that both our learning and masking defense can work simultaneously to protect against multiple attacks. We demonstrate the efficacy of our techniques by experimenting with the MNIST and CIFAR-10 datasets.