The deep hashing based retrieval method is widely adopted in large-scale image and video retrieval. However, there is little investigation on its security. In this paper, we propose a novel method, dubbed deep hashing targeted attack (DHTA), to study the targeted attack on such retrieval. Specifically, we first formulate the targeted attack as a point-to-set optimization, which minimizes the average distance between the hash code of an adversarial example and those of a set of objects with the target label. Then we design a novel component-voting scheme to obtain an anchor code as the representative of the set of hash codes of objects with the target label, whose optimality guarantee is also theoretically derived. To balance the performance and perceptibility, we propose to minimize the Hamming distance between the hash code of the adversarial example and the anchor code under the $\ell^\infty$ restriction on the perturbation. Extensive experiments verify that DHTA is effective in attacking both deep hashing based image retrieval and video retrieval.

本文提出了一种深度哈希目标攻击方法(DHTA)，通过点集优化将对抗样本哈希码的平均距离最小化来攻击具有目标标签的对象的哈希，并提出了一种新的组件投票方案来获得锚码作为目标标签对象的哈希码集合的代表，以在某些限制条件下，平衡性能和可感知性。实验表明，DHTA对深度哈希模型的图像和视频检索均有效。

基于深度哈希的检索的有针对性攻击