PASTA: A Patch-Agnostic Twofold-Stealthy Backdoor Attack on Vision Transformers
Vision Transformers (ViTs) have achieved remarkable success across vision tasks, yet recent studies show they remain vulnerable to backdoor attacks. Existing patch-wise attacks typically assume a single fixed trigger location during inference to maximize trigger attention. However, they overlook the self-attention mechanism in ViTs, which captures long-range dependencies across patches. In this work, we observe that a patch-wise trigger can achieve high attack effectiveness when activating backdoors across neighboring patches, a phenomenon we term the Trigger Radiating Effect (TRE). We further find that inter-patch trigger insertion during training can synergistically enhance TRE compared...
| Authors | Dazhuang Liu, Yanqi Qiao, Rui Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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