Reliable And Efficient Evaluation Of Adversarial Robustness For Deep Hashing-based Retrieval

Abstract

Deep hashing has been extensively applied to massive image retrieval due to its efficiency and effectiveness. Recently, several adversarial attacks have been presented to reveal the vulnerability of deep hashing models against adversarial examples. However, existing attack methods suffer from degraded performance or inefficiency because they underutilize the semantic relations between original samples or spend a lot of time learning these relations with a deep neural network. In this paper, we propose a novel Pharos-guided Attack, dubbed PgA, to evaluate the adversarial robustness of deep hashing networks reliably and efficiently. Specifically, we design pharos code to represent the semantics of the benign image, which preserves the similarity to semantically relevant samples and dissimilarity to irrelevant ones. It is proven that we can quickly calculate the pharos code via a simple math formula. Accordingly, PgA can directly conduct a reliable and efficient attack on deep hashing-bas

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keywang2023reliable

Related papers

• Semantic-aware Adversarial Training For Reliable Deep Hashing Retrieval (2023)13.49
• Targeted Attack For Deep Hashing Based Retrieval (2020)13.65
• Cgat: Center-guided Adversarial Training For Deep Hashing-based Retrieval (2022)8.42
• Unsupervised Multi-criteria Adversarial Detection In Deep Image Retrieval (2023)0.00
• Deep Self-adaptive Hashing For Image Retrieval (2021)8.35
• Prototype-supervised Adversarial Network For Targeted Attack Of Deep Hashing (2021)15.97
• Darkhash: A Data-free Backdoor Attack Against Deep Hashing (2025)2.26
• Query-adaptive Image Retrieval By Deep Weighted Hashing (2016)12.68