HUANG: A Robust Diffusion Model-based Targeted Adversarial Attack Against Deep Hashing Retrieval
Year: 2025
Type: conference-paper
Abstract: Deep hashing models have achieved great success in retrieval tasks due to their powerful representation and strong information compression capabilities. However, they inherit the vulnerability of deep neural networks to adversarial perturbations. Attackers can severely impact the retrieval capability of hashing models by adding subtle, carefully crafted adversarial perturbations to benign images, transforming them into adversarial images. Most existing adversarial attacks target image classification models, with few focusing on retrieval models. We propose HUANG, the first targeted adversarial attack algorithm to leverage a diffusion model for hashing retrieval in black-box scenarios. In our approach, adversarial denoising uses adversarial perturbations and residual image to guide the shift from benign to adversarial distribution. Extensive experiments demonstrate the superiority of HUANG across different datasets, achieving state-of-the-art performance in black-box targeted attacks. Additionally, the dynamic interplay between denoising and adding adversarial perturbations in adversarial denoising endows HUANG with exceptional robustness and transferability.
Authors: Chihan Huang, Xiaobo Shen
Institution: Nanjing University of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Awards: G5830298717, G7997069161, G8730268472
OpenAlex ID: W4409368362