Decision-Based Adversarial Attack With Frequency Mixup
Xiu-Chuan Li, Xu-Yao Zhang, Fei Yin, Cheng‐Lin Liu · IEEE Transactions on Information Forensics and Security · 2022
It has been widely observed that deep neural networks are highly vulnerable to adversarial examples. Decision-based attacks could generate adversarial examples based solely on top-1 labels returned by the target model. However, they typically make excessive queries and could not bypass detection effectively. To comprehensively assess a decision-based attack, besides its query efficiency, the performance against detection is also a concern. Considering that previous detections consume massive resources and always mistakenly recognize benign video frames as malicious attacks, we design a lightweight detection calledboundary detectionto overcome the above limitations, whose success reveals serious limitations of existing decision-based attacks. To develop more powerful attacks, we first presentf-mixupas a basic method to produce candidate adversarial examples in the frequency domain. Usingf-mixupas the building block, we proposef-attackas a complete decision-based attack. With the help of several natural images,f-attackcould both work well with limited (hundreds of) queries and bypass detection effectively. Nevertheless, if the attacker could make relatively adequate (thousands of) queries and the target model is not equipped with detection,f-attackwill lag behind existing decision-based attacks. We additionally introducefrequency binary searchbased onf-mixup, which serves as a plug-and-play module for existing decision-based attacks to further improve their query efficiency. Experimental results verify the effectiveness of our proposed methods.