PatchCensor: Patch Robustness Certification for Transformers via Exhaustive Testing

Understand

Vision Transformer (ViT) is known to be highly nonlinear like other classical neural networks and could be easily fooled by both natural and adversarial patch perturbations.

• This limitation could pose a threat to the deployment of ViT in the real industrial environment, especially in safety-critical scenarios.
• In this work, we propose PatchCensor, aiming to certify the patch robustness of ViT by applying exhaustive testing.
• We try to provide a provable guarantee by considering the worst patch attack scenarios.