GaitReload: A Reloading Framework for Defending Against On-Manifold Adversarial Gait Sequences

Abstract

Recent on-manifold adversarial attacks can mislead gait recognition by generating adversarial walking postures (AWP) with image generation techniques. However, existing defense methods only eliminate adversarial perturbations on each frame isolatedly but ignore the temporal correlation of gait sequence, which leads to vulnerability of robust gait recognition. In this paper, we propose GaitReload, a post-processing adversarial defense method to defend against AWP for the gait recognition model with sequenced inputs. First, GaitReload utilizes sequenced entity recognition (SER) module to detect the adversarial frames by the temporal constraints of gait sequence. Then, we apply bayesian uncertainty filtering-based (BUF-based) gait interpolation to reform adversarial gait examples. After that, we reload the reformed gait sequence and rectify the recognition results with the guidance of reloading strategy. Specifically, SER has a bi-directional frame difference attention and a temporal feature aggregation to boost the detection performance. For training SER, we apply hidden posture selective attack (HPSA) to generate training samples. The extensive experimental results on CASIA-A, CASIA-B, and OU-ISIR demonstrate that GaitReload can defend against adversarial gait by large margins in both RGB and silhouette modes.