# Targeted Attack for Deep Hashing based Retrieval

arXiv: https://arxiv.org/abs/2004.07955 · HTML: https://mlsanity.com/paper/2004.07955/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

## Built on by (most cited first, 30 of 35)

- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [LAS-AT: Adversarial Training with Learnable Attack Strategy](https://mlsanity.com/paper/2203.06616/) (2022)
- [Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection](https://mlsanity.com/paper/2210.00875/) (2022)
- [Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images](https://mlsanity.com/paper/2401.11170/) (2024)
- [A Large-scale Multiple-objective Method for Black-box Attack against Object Detection](https://mlsanity.com/paper/2209.07790/) (2022)
- [Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits](https://mlsanity.com/paper/2102.10496/) (2021)
- [Backdoor Defense via Adaptively Splitting Poisoned Dataset](https://mlsanity.com/paper/2303.12993/) (2023)
- [Prior-Guided Adversarial Initialization for Fast Adversarial Training](https://mlsanity.com/paper/2207.08859/) (2022)
- [BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label](https://mlsanity.com/paper/2207.00278/) (2022)
- [Untargeted Backdoor Attack against Object Detection](https://mlsanity.com/paper/2211.05638/) (2022)
- [Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash](https://mlsanity.com/paper/2111.06628/) (2021)
- [Adversarial Robustness for Visual Grounding of Multimodal Large Language Models](https://mlsanity.com/paper/2405.09981/) (2024)
- [Hardly Perceptible Trojan Attack against Neural Networks with Bit Flips](https://mlsanity.com/paper/2207.13417/) (2022)
- [One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training](https://mlsanity.com/paper/2308.07934/) (2023)
- [Versatile Weight Attack via Flipping Limited Bits](https://mlsanity.com/paper/2207.12405/) (2022)
- [Backdoor Attack on Hash-based Image Retrieval via Clean-label Data Poisoning](https://mlsanity.com/paper/2109.08868/) (2021)
- [DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain](https://mlsanity.com/paper/2410.12307/) (2024)
- [Backdoor Defense via Suppressing Model Shortcuts](https://mlsanity.com/paper/2211.05631/) (2022)
- [Deep Hash Distillation for Image Retrieval](https://mlsanity.com/paper/2112.08816/) (2021)
- [Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing](https://mlsanity.com/paper/2105.07553/) (2021)
- [GSE: Group-wise Sparse and Explainable Adversarial Attacks](https://mlsanity.com/paper/2311.17434/) (2023)
- [A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking](https://mlsanity.com/paper/2306.12111/) (2023)
- [Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval](https://mlsanity.com/paper/2310.14637/) (2023)
- [Protecting Your Video Content: Disrupting Automated Video-based LLM Annotations](https://mlsanity.com/paper/2503.21824/) (2025)
- [Backdoor Learning for NLP: Recent Advances, Challenges, and Future Research Directions](https://mlsanity.com/paper/2302.06801/) (2023)
- [Anomaly Detection of Adversarial Examples using Class-conditional Generative Adversarial Networks](https://mlsanity.com/paper/2105.10101/) (2021)
- [AutoSSVH: Exploring Automated Frame Sampling for Efficient Self-Supervised Video Hashing](https://mlsanity.com/paper/2504.03587/) (2025)
- [CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval](https://mlsanity.com/paper/2204.10779/) (2022)
- [Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval](https://mlsanity.com/paper/2312.07364/) (2023)
- [ARIA: Adversarially Robust Image Attribution for Content Provenance](https://mlsanity.com/paper/2202.12860/) (2022)

## Abstract (arXiv)

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 ℓ^∞ restriction on the perturbation. Extensive experiments verify that DHTA is effective in attacking both deep hashing based image retrieval and video retrieval.