Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer

2 code implementations • 21 Aug 2023 • Zhijin Ge, Fanhua Shang, Hongying Liu, Yuanyuan Liu, Liang Wan, Wei Feng, Xiaosen Wang

Deep neural networks are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on clean inputs.

Domain Generalization Style Transfer

Boosting Adversarial Transferability by Achieving Flat Local Maxima

2 code implementations • NeurIPS 2023 • Zhijin Ge, Hongying Liu, Xiaosen Wang, Fanhua Shang, Yuanyuan Liu

Extensive experimental results on the ImageNet-compatible dataset show that the proposed method can generate adversarial examples at flat local regions, and significantly improve the adversarial transferability on either normally trained models or adversarially trained models than the state-of-the-art attacks.