Boosting the Local Invariance for Better Adversarial Transferability

Summary

Researchers propose LI-Boost, a technique that improves how well adversarial perturbations transfer between models. They find that transferability correlates positively with the local invariance of perturbations relative to the input image. On ImageNet, LI-Boost enhances five categories of transfer-based attacks, with gains reported on CNNs, ViTs, defenses, commercial vision APIs and vision-language models.

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