A Unified Approach to Interpreting and Boosting Adversarial Transferability

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Authors Xin Wang, Jie Ren, Shuyun Lin, Xiangming Zhu, Yisen Wang, Quanshi Zhang arXiv ID 2010.04055 Category cs.LG: Machine Learning Cross-listed cs.AI, cs.CV Citations 110 Venue International Conference on Learning Representations Last Checked 4 months ago
Abstract
In this paper, we use the interaction inside adversarial perturbations to explain and boost the adversarial transferability. We discover and prove the negative correlation between the adversarial transferability and the interaction inside adversarial perturbations. The negative correlation is further verified through different DNNs with various inputs. Moreover, this negative correlation can be regarded as a unified perspective to understand current transferability-boosting methods. To this end, we prove that some classic methods of enhancing the transferability essentially decease interactions inside adversarial perturbations. Based on this, we propose to directly penalize interactions during the attacking process, which significantly improves the adversarial transferability.
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