Clean Image May Be Dangerous: Data Poisoning Attacks Against Deep Hashing

Abstract

Large-scale image retrieval using deep hashing has become increasingly popular due to the exponential growth of image data and the remarkable feature extraction capabilities of deep neural networks (DNNs). However, deep hashing methods are vulnerable to malicious attacks, including adversarial and backdoor attacks. It is worth noting that these attacks typically involve altering the query images, which is not a practical concern in real-world scenarios. In this paper, we point out that even clean query images can be dangerous, inducing malicious target retrieval results, like undesired or illegal images. To the best of our knowledge, we are the first to study data \textbf\{p\}oisoning \textbf\{a\}ttacks against \textbf\{d\}eep \textbf\{hash\}ing \textbf\{(\textit\{PADHASH\})\}. Specifically, we first train a surrogate model to simulate the behavior of the target deep hashing model. Then, a strict gradient matching strategy is proposed to generate the poisoned images. Extensive experime

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyli2025clean

Related papers

• Badhash: Invisible Backdoor Attacks Against Deep Hashing With Clean Label (2022)11.19
• Darkhash: A Data-free Backdoor Attack Against Deep Hashing (2025)2.26
• Backdoor Attack On Hash-based Image Retrieval Via Clean-label Data Poisoning (2021)2.51
• Model Inversion Attack Against Deep Hashing (2025)0.00
• Diffhash: Text-guided Targeted Attack Via Diffusion Models Against Deep Hashing Image Retrieval (2025)0.00
• Targeted Attack For Deep Hashing Based Retrieval (2020)13.65
• Deep Self-adaptive Hashing For Image Retrieval (2021)8.35
• Unsupervised Multi-criteria Adversarial Detection In Deep Image Retrieval (2023)0.00