FG-UAP: Feature-Gathering Universal Adversarial Perturbation

TL;DR

This paper introduces FG-UAP, a novel universal adversarial perturbation method that exploits neural collapse to effectively attack deep neural networks, including robust architectures like Vision Transformers.

Contribution

The paper proposes a new feature-gathering universal adversarial perturbation method based on neural collapse, enhancing attack effectiveness across various models and settings.

Findings

FG-UAP effectively attacks multiple architectures including Vision Transformers.

Neural collapse intensifies after model corruption, aiding adversarial attacks.

FG-UAP performs well in limited data and black-box attack scenarios.

Abstract

Deep Neural Networks (DNNs) are susceptible to elaborately designed perturbations, whether such perturbations are dependent or independent of images. The latter one, called Universal Adversarial Perturbation (UAP), is very attractive for model robustness analysis, since its independence of input reveals the intrinsic characteristics of the model. Relatively, another interesting observation is Neural Collapse (NC), which means the feature variability may collapse during the terminal phase of training. Motivated by this, we propose to generate UAP by attacking the layer where NC phenomenon happens. Because of NC, the proposed attack could gather all the natural images' features to its surrounding, which is hence called Feature-Gathering UAP (FG-UAP). We evaluate the effectiveness our proposed algorithm on abundant experiments, including untargeted and targeted universal attacks, attacks under limited dataset, and transfer-based black-box attacks among different architectures including Vision Transformers, which are believed to be more robust. Furthermore, we investigate FG-UAP in the view of NC by analyzing the labels and extracted features of adversarial examples, finding that collapse phenomenon becomes stronger after the model is corrupted. The code will be released when the paper is accepted.