Targeted Attack For Deep Hashing Based Retrieval
2020 Β· Jiawang Bai, Bin Chen, Yiming Li, et al.
Abstract
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 at
Authors
(none)
Tags
Stats
Related papers
- Diffhash: Text-guided Targeted Attack Via Diffusion Models Against Deep Hashing Image Retrieval (2025)0.00
- Unsupervised Multi-criteria Adversarial Detection In Deep Image Retrieval (2023)0.00
- Cgat: Center-guided Adversarial Training For Deep Hashing-based Retrieval (2022)8.42
- Darkhash: A Data-free Backdoor Attack Against Deep Hashing (2025)2.26
- Transfer Adversarial Hashing For Hamming Space Retrieval (2017)8.60
- Reliable And Efficient Evaluation Of Adversarial Robustness For Deep Hashing-based Retrieval (2023)0.00
- Semantic-aware Adversarial Training For Reliable Deep Hashing Retrieval (2023)13.49
- Prototype-supervised Adversarial Network For Targeted Attack Of Deep Hashing (2021)15.97