In recent years, there has been significant research focusing on addressing
security concerns in single-modal person re-identification (ReID) systems that
are based on RGB images. However, the safety of cross-modality scenarios, which
are more commonly encountered in practical applications involving images
captured by infrared cameras, has not received adequate attention. The main
challenge in cross-modality ReID lies in effectively dealing with visual
differences between different modalities. For instance, infrared images are
typically grayscale, unlike visible images that contain color information.
Existing attack methods have primarily focused on the characteristics of the
visible image modality, overlooking the features of other modalities and the
variations in data distribution among different modalities. This oversight can
potentially undermine the effectiveness of these methods in image retrieval
across diverse modalities. This study represents the first exploration into the
security of cross-modality ReID models and proposes a universal perturbation
attack specifically designed for cross-modality ReID. This attack optimizes
perturbations by leveraging gradients from diverse modality data, thereby
disrupting the discriminator and reinforcing the differences between
modalities. We conducted experiments on two widely used cross-modality
datasets, namely RegDB and SYSU, which not only demonstrated the effectiveness
of our method but also provided insights for future enhancements in the
robustness of cross-modality ReID systems.

本研究针对跨模态人物再识别（ReID）模型的安全性进行了首次探索，并提出了一种专门针对跨模态 ReID 的通用扰动攻击。该攻击利用不同模态数据的梯度优化扰动，从而破坏鉴别器并加强模态之间的差异，通过在 RegDB 和 SYSU 两个广泛使用的跨模态数据集上的实验证明了该方法的有效性，并为未来增强跨模态 ReID 系统的鲁棒性提供了见解。