Understanding Robustness of Visual State Space Models for Image
  Classification
Chengbin Du Yanxi Li Chang Xu
Visual State Space Model (VMamba) has recently emerged as a promising
architecture, exhibiting remarkable performance in various computer vision
tasks. However, its robustness has not yet been thoroughly studied. In this
paper, we delve into the robustness of this architecture through comprehensive
investigations from multiple perspectives. Firstly, we investigate its
robustness to adversarial attacks, employing both whole-image and
patch-specific adversarial attacks. Results demonstrate superior adversarial
robustness compared to Transformer architectures while revealing scalability
weaknesses. Secondly, the general robustness of VMamba is assessed against
diverse scenarios, including natural adversarial examples, out-of-distribution
data, and common corruptions. VMamba exhibits exceptional generalizability with
out-of-distribution data but shows scalability weaknesses against natural
adversarial examples and common corruptions. Additionally, we explore VMamba's
gradients and back-propagation during white-box attacks, uncovering unique
vulnerabilities and defensive capabilities of its novel components. Lastly, the
sensitivity of VMamba to image structure variations is examined, highlighting
vulnerabilities associated with the distribution of disturbance areas and
spatial information, with increased susceptibility closer to the image center.
Through these comprehensive studies, we contribute to a deeper understanding of
VMamba's robustness, providing valuable insights for refining and advancing the
capabilities of deep neural networks in computer vision applications.