ReGeNet: Relevance-Guided Generative Network to Evaluate the Adversarial Robustness of Cross-Modal Retrieval Systems

Streaming media data have become pervasive in modern commercial systems. To address large-scale data processing in intelligent transportation systems (ITSs), recent research has focused on deep neural network–based (DNN-based) approaches to improve the performance of cross-modal hashing retrieval (CMHR) systems. However, due to their high dimensionality and network depth, DNN-based CMHR systems inherently suffer from vulnerabilities to malicious adversarial examples (AEs). This paper investigates the robustness of CMHR-based ITS systems against AEs. Prior work typically formulates AE generation as an optimization-driven, iterative process, whose high computational cost and slow generation speed limit research efficiency. To overcome these limitations, we propose a parallel cross-modal relevance-guided generative network (ReGeNet) that captures the semantic characteristics of the target deep hashing model. During training, we design a relevance-guided adversarial generative framework to efficiently learn AE generation. During inference, the well-trained parallel adversarial generator produces adversarial cross-modal data with effectiveness comparable to that of iterative methods. Experimental results demonstrate that ReGeNet can generate AEs significantly faster while achieving competitive attack performance relative to iterative-based approaches.