Boosting Adversarial Transferability by Block Shuffle and Rotation

arXiv:2308.10299v3 Announce Type: replace
Abstract: Adversarial examples mislead deep neural networks with imperceptible perturbations and have brought significant threats to deep learning. An important aspect is their transferability, which refers to their ability to deceive other models, thus enabling attacks in the black-box setting. Though various methods have been proposed to boost transferability, the performance still falls short compared with white-box attacks. In this work, we observe that existing input transformation based attacks, one of the mainstream transfer-based attacks, result …

adversarial arxiv block boosting cs.cv eess.iv rotation type