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Deep neural networks are vulnerable to adversarial examples, dictating theimperativeness to test the model's robustness before deployment. Transfer-basedattackers craft adversarial examples against surrogate models and transfer themto victim models deployed in the black-box situation. To enhance theadversarial transferability, structure-based attackers adjust thebackpropagation path to avoid the attack from overfitting the surrogate model.However, existing structure-based attackers fail to explore the convolutionmodule in CNNs and modify the backpropagation graph heuristically, leading tolimited effectiveness. In this paper, we propose backPropagation pAth Search(PAS), solving the aforementioned two problems. We first propose SkipConv toadjust the backpropagation path of convolution by structuralreparameterization. To overcome the drawback of heuristically designedbackpropagation paths, we further construct a DAG-based search space, utilizeone-step approximation for path evaluation and employ Bayesian Optimization tosearch for the optimal path. We conduct comprehensive experiments in a widerange of transfer settings, showing that PAS improves the attack success rateby a huge margin for both normally trained and defense models.

Backpropagation Path Search On Adversarial Transferability