Exploring Targeted Universal Adversarial Attack for Deep Hashing

Fei Zhu, Wanqian Zhang, Dayan Wu, Lin Wang, Bo Li, Weiping Wang

Abstract: Although image-dependent adversarial attacks have been studied, the more challenging image-agnostic adversarial attack for deep hashing remains an unexplored territory. In this paper, we take the first attempt on the more efficient and malicious targeted universal adversarial attack (TUAA) for deep hashing. When previous image-dependent attacks are directly applied to TUAA task, they usually face two main issues. Firstly, existing anchor code generation methods generate anchor code with inferior representative semantic-preserving ability. Secondly, previous methods simply minimize the distance between hash codes and anchor code in Hamming space, which tends to optimize targeted universal adversarial perturbation (TUAP) in a coarse-grained manner. To tackle the above problems, we propose a Semantic-enhanced and Stabilized Targeted Universal Adversarial Attack (SS-TUAA) method. Specifically, we first propose a new Candidate Anchor code Evaluation (CAE) method to generate anchor code with superior semantic-preserving ability. Then, to enhance the ‘dominant role’ and the stability of TUAP, we propose a Feature Consistency Loss (FCL) to align the fine-grained feature representation between TUAP and adversarial examples. Extensive experiments demonstrate the effectiveness of each component within our SS-TUAA method, and our method can achieve the state-of-the-art TUAA performance for deep hashing.