With the great development of generative model techniques, face forgery
detection draws more and more attention in the related field. Researchers find
that existing face forgery models are still vulnerable to adversarial examples
with generated pixel perturbations in the global image. These generated
adversarial samples still can't achieve satisfactory performance because of the
high detectability. To address these problems, we propose an Adversarial
Semantic Mask Attack framework (ASMA) which can generate adversarial examples
with good transferability and invisibility. Specifically, we propose a novel
adversarial semantic mask generative model, which can constrain generated
perturbations in local semantic regions for good stealthiness. The designed
adaptive semantic mask selection strategy can effectively leverage the class
activation values of different semantic regions, and further ensure better
attack transferability and stealthiness. Extensive experiments on the public
face forgery dataset prove the proposed method achieves superior performance
compared with several representative adversarial attack methods. The code is
publicly available at this https URL

提出了一种具有良好迁移性和隐蔽性的对抗语义掩码攻击框架 (ASMA)，该框架使用了语义掩码来生成具有局部语义区域中的扰动的对抗样本，并通过自适应语义掩码选择策略提高了攻击的迁移性和隐蔽性。与其他对抗攻击方法相比，该方法在公共人脸伪造数据集上具有卓越的性能。