  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.
