FPAD: Fuzzy-Prototype-Guided Adversarial Attack and Defense for Deep Cross-Modal Hashing

作者：Zhongqing Yu, Xin Liu, Yiu‐ming Cheung, Lei Zhu, Xing Xu, Nannan Wang · 发表于：IEEE Transactions on Circuits and Systems for Video Technology · 年份：2025 · DOI：10.1109/tcsvt.2025.3604033 · 被引用次数：2 · 研究领域：Advanced Image and Video Retrieval Techniques、Video Surveillance and Tracking Methods、Digital Media Forensic Detection

Deep cross-modal hashing models generally inherit the vulnerabilities of deep neural networks, making them susceptible to adversarial attacks and thus posing a serious security risk during real-world deployment. Current adversarial attack or defense strategies often establish a weak correlation between the hashing codes and the targeted semantic representations, and there is still a lack of related works that simultaneously consider the attack and defense for deep cross-modal hashing. To alleviate these concerns, we propose a Fuzzy-Prototype-guided Adversarial Attack and Defense (FPAD) framework to enhance the adversarial robustness of deep cross-modal hashing models. First, an adaptive fuzzy-prototype learning network (FpNet) is efficiently presented to extract a set of fuzzy-prototypes, aiming to encode the underlying semantic structure of the heterogeneous modalities in both feature and Hamming spaces. Then, these derived prototypical hash codes are heuristically employed to supervise the generation of high-quality adversarial examples, while a fuzzy-prototype rectification scheme is simultaneously designed to preserve the latent semantic consistency between the adversarial and benign examples. By mixing the adversarial samples with the original training samples as the augmented inputs, an efficient fuzzy-prototype-guided adversarial learning framework is proposed to execute the collaborative adversarial training and generate robust cross-modal hash codes with high adversa...