Efficient Non-Targeted Attack for Deep Hashing Based Image Retrieval

Chuan Qin, Liang Wu, Xinpeng Zhang, Guorui Feng

Abstract: As the deep hashing technique has been widely used in large-scale image retrieval, the corresponding security issue is getting more and more attention. Recent studies have found that deep image classifiers are vulnerable to adversarial example attacks and produce misleading classifications. Therefore, in order to study the robustness of deep hashing based retrieval system to adversarial example, in this letter, we propose a novel adversarial example generation algorithm, non-targeted deep hashing attack (NDHA), which uses the anchor-moving strategy to continuously modify anchor image to cross the search correlation boundary and maximize Hamming distance between the hash codes of adversarial example and query image. The generated adversarial example can make the retrieved result semantically irrelevant to query image. Extensive experiments show that the proposed NDHA can efficiently produce imperceptible perturbation, which is effective for attacking deep hashing based retrieval systems.