Query-Side Adversarial Attacks on Event-Based Person Re-Identification: A First-Order Robustness Analysis

Event-based person re-identification (Re-ID) has recently emerged as a privacy-friendly alternative to conventional RGB-based surveillance. However, the security and adversarial robustness of these systems remain largely understudied. This paper presents a systematic investigation into the vulnerabilities of event-based person Re-ID models operating on 5-channel event voxels. We evaluate the impact of a one-step FGSM attack on query-side event voxel inputs and measure the resulting retrieval performance. Our experiments demonstrate a significant susceptibility: under subtle perturbations, the Top-1 accuracy drops drastically from 0.462 to 0.154. Critically, these adversarial inputs maintain high perceptual similarity to the original data, with an average SSIM of approximately 0.99 and an average PSNR of 45 dB, rendering the modifications nearly imperceptible. These findings suggest that the sparse and asynchronous nature of event-based person Re-ID, despite its potential privacy advantages, is highly susceptible to gradient-based exploits. This study highlights the need for robustness-aware design and defense mechanisms in event-based surveillance systems.