Feature-Level Adversarial Attacks and Ranking Disruption for Visible-Infrared Person Re-identification

Visible-infrared person re-identification (VIReID) is widely used in fields such as video surveillance and intelligent transportation, imposing higher demands on model security. In practice, the adversarial attacks based on VIReID aim to disrupt output ranking and quantify the security risks of models. Although numerous studies have been emerged on adversarial attacks and defenses in fields such as face recognition, person re-identification, and pedestrian detection, there is currently a lack of research on the security of VIReID systems. To this end, we propose to explore the vulnerabilities of VIReID systems and prevent potential serious losses due to insecurity. Compared to research on single-modality ReID, adversarial feature alignment and modality differences need to be particularly emphasized. Thus, we advocate for feature-level adversarial attacks to disrupt the output rankings of VIReID systems. To obtain adversarial features, we introduce Universal Adversarial Perturbations (UAP) to simulate common disturbances in real-world environments. Additionally, we employ a Frequency-Spatial Attention Module (FSAM), integrating frequency information extraction and spatial focusing mechanisms, and further emphasize important regional features from different domains on the shared features. This ensures that adversarial features maintain consistency within the feature space. Finally, we employ an Auxiliary Quadruple Adversarial Loss to amplify the differences between modalities, thereby improving the distinction and recognition of features between visible and infrared images, which cause the system to output incorrect rankings. Extensive experiments on two VIReID benchmarks (i.e., SYSU-MM01, RegDB) and different systems validate the effectiveness of our method.