Towards Transferable Adversarial Attacks on Vision Transformers. (arXiv:2109.04176v3 [cs.CV] UPDATED)

Vision transformers (ViTs) have demonstrated impressive performance on a
series of computer vision tasks, yet they still suffer from adversarial
examples. % crafted in a similar fashion as CNNs. In this paper, we posit that
adversarial attacks on transformers should be specially tailored for their
architecture, jointly considering both patches and self-attention, in order to
achieve high transferability. More specifically, we introduce a dual attack
framework, which contains a Pay No Attention (PNA) attack and a PatchOut
attack, to improve …

arxiv attacks cv transformers