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.

中文翻译：

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GaitReload：一种用于防御流形对抗步态序列的重载框架

最近的流形对抗攻击可以通过使用图像生成技术生成对抗性行走姿势 (AWP) 来误导步态识别。然而，现有的防御方法只是孤立地消除了每一帧上的对抗性扰动，而忽略了步态序列的时间相关性，这导致了鲁棒步态识别的脆弱性。在本文中，我们提出了 GaitReload，这是一种后处理对抗防御方法，用于防御具有顺序输入的步态识别模型的 AWP。首先，GaitReload 利用序列实体识别 (SER) 模块通过步态序列的时间约束来检测对抗帧。然后，我们应用基于贝叶斯不确定性过滤（基于 BUF）的步态插值来改造对抗性步态示例。在那之后，我们重新加载改革后的步态序列，并在重新加载策略的指导下纠正识别结果。具体来说，SER 具有双向帧差异注意力和时间特征聚合以提高检测性能。为了训练 SER，我们应用隐藏姿势选择性攻击 (HPSA) 来生成训练样本。在 CASIA-A、CASIA-B 和 OU-ISIR 上的广泛实验结果表明，GaitReload 可以在 RGB 和轮廓模式下大幅抵御对抗性步态。