AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack

TL;DR

AutoAdversary introduces an end-to-end neural network-based method for generating sparse adversarial examples by automatically selecting critical pixels, improving efficiency and effectiveness over heuristic approaches.

Contribution

It proposes a novel integrated approach combining pixel selection and adversarial attack into a single trainable framework, unlike prior heuristic methods.

Findings

Outperforms state-of-the-art sparse attack methods.

Maintains efficiency with increasing image size.

Automatically identifies critical pixels for perturbation.

Abstract

Deep neural networks (DNNs) have been proven to be vulnerable to adversarial examples. A special branch of adversarial examples, namely sparse adversarial examples, can fool the target DNNs by perturbing only a few pixels. However, many existing sparse adversarial attacks use heuristic methods to select the pixels to be perturbed, and regard the pixel selection and the adversarial attack as two separate steps. From the perspective of neural network pruning, we propose a novel end-to-end sparse adversarial attack method, namely AutoAdversary, which can find the most important pixels automatically by integrating the pixel selection into the adversarial attack. Specifically, our method utilizes a trainable neural network to generate a binary mask for the pixel selection. After jointly optimizing the adversarial perturbation and the neural network, only the pixels corresponding to the value 1 in the mask are perturbed. Experiments demonstrate the superiority of our proposed method over several state-of-the-art methods. Furthermore, since AutoAdversary does not require a heuristic pixel selection process, it does not slow down excessively as other methods when the image size increases.