Gait recognition is a non-intrusive method that captures unique walking patterns without subject cooperation, which has emerged as a promising technique across various fields. Recent studies based on Deep Neural Networks (DNNs) have notably improved the performance, however, the potential vulnerability inherent in DNNs and their resistance to interference in practical gait recognition systems remain under-explored. To fill the gap, in this paper, we focus on imperceptible adversarial attack for deep gait recognition and propose an edge-oriented attack strategy tailored for silhouette-based approaches. Specifically, we make a pioneering attempt to explore the intrinsic characteristics of binary silhouettes, with a primary focus on injecting noise perturbations into the edge area. This simple yet effective solution enables sparse attack in both the spatial and temporal dimensions, which largely ensures imperceptibility and simultaneously achieves high success rate. In particular, our solution is built on a unified framework, allowing seamless switching between untargeted and targeted attack modes. Extensive experiments conducted on in-the-lab and in-the-wild benchmarks validate the effectiveness of our attack strategy and emphasize the necessity to study adversarial attack and defense strategy in the near future.

步态识别是一种非侵入性方法，无需受试者合作即可捕捉独特的行走模式，这已成为各个领域中一种很有前途的技术。最近基于深度神经网络 （DNN） 的研究显着提高了性能，但是，DNN 固有的潜在漏洞及其对实际步态识别系统干扰的抵抗力仍未得到充分探索。为了填补这一空白，在本文中，我们专注于用于深度步态识别的难以察觉的对抗性攻击，并提出了一种为基于轮廓的方法量身定制的面向边缘的攻击策略。具体来说，我们开创性地尝试探索二进制轮廓的内在特征，主要关注将噪声扰动注入边缘区域。这种简单而有效的解决方案可以在空间和时间维度上实现稀疏攻击，在很大程度上保证了不可察觉性，同时实现了较高的成功率。特别是，我们的解决方案建立在统一的框架之上，允许在非针对性和针对性攻击模式之间无缝切换。在实验室和野外基准测试中进行的广泛实验验证了我们的攻击策略的有效性，并强调了在不久的将来研究对抗性攻击和防御策略的必要性。