Precise Target-Oriented Attack against Deep Hashing-based Retrieval

Deep hashing has been widely applied in large-scale image retrieval due to its powerful computational efficiency. Nevertheless, the vulnerability of deep hashing to adversarial examples has been revealed, particularly to targeted attacks with stronger manipulability. Existing targeted attack methods for deep hashing default to selecting target labels from random images, usually encompassing multiple classes for attack in multi-label datasets. However, they exhabit poor performance when facing a preciser single target label selection. In this work, we propose a novel Precise Target-Oriented Attack dubbed PTA, to enhance the precision of such targeted attacks. Specifically, we further categorize the general target label into preciser single target label for attack. By relaxing the non-differentiable indicator function, we directly adopt Average Precision (AP) as optimization objective to guide the generation of adversarial examples on a small subset of the entire database, thus achieving stronger precision. Extensive experiments demonstrate that the proposed PTA achieves state-of-the-art performance in both general and single target label selection, with superior transferability and universality.