Gait authentication, especially sensor-based patterns, has been studied by researchers for decades. Nowadays, gait authentication has become an important facet of biometric systems due to the so-called unique characteristics of each user. With the development of various technologies (i.e., hardware, data processing, features extraction, and learning algorithms), the performance of sensor-based authentication methods is gradually improving. But we have found that the vulnerability of most existing methods can be compromised easily. In this paper, we propose a novel attack model, called one cycle attack, to bypass existing gait authentication methods. Firstly, the gait sequence is divided into multiple gait cycles. By adopting the K-mean algorithm, we get the average distance of each feature sample (extracted from the gait cycle) to its closest cluster center, and its result confirms that independent individuals may have similar gait cycles. Secondly, using six state-of-the-art models it was found that the adversarial gait cycle found with the clustering method can bypass the victim’s model rapidly. Furthermore, to improve the accuracy of sensor-based gait authentication methods to fight against attacks, we present a WPD-LSTM (Wavelet Packet Decomposition and Long Short-Term Memory) multi-cycle defense model which considers the contextual contents of the neighboring gait cycles in the gait sequence. Experimental results on two datasets (the largest public sensor-based gait database OU-ISIR and new dataset from our laboratory) show that our attack model can bypass most of the victims’ models within a limited number of attempts. Specifically, we can compromise 20%–80% of users within 5 attempts by utilizing imitation. On the contrary, the success rate of attackers has been greatly mitigated by deploying our multi-cycle defense model.

中文翻译：

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一周期攻击：具有集群功能的基于傻瓜传感器的个人步态认证

步态认证，尤其是基于传感器的模式，已经由研究人员研究了数十年。如今，由于每个用户的所谓独特特征，步态认证已成为生物识别系统的重要方面。随着各种技术（即硬件，数据处理，特征提取和学习算法）的发展，基于传感器的身份验证方法的性能正在逐步提高。但是我们发现，大多数现有方法的脆弱性很容易受到损害。在本文中，我们提出了一种新颖的攻击模型，称为单周期攻击，以绕过现有的步态认证方法。首先，步态序列分为多个步态周期。通过采用K均值算法，我们得到每个特征样本（从步态周期中提取）到其最近的聚类中心的平均距离，其结果证实独立的个体可能具有相似的步态周期。其次，使用六个最新模型，发现使用聚类方法发现的对抗步态周期可以快速绕过受害者的模型。此外，为了提高基于传感器的步态认证方法抵抗攻击的准确性，我们提出了一种WPD-LSTM（小波包分解和长短期记忆）多周期防御模型，该模型考虑了相邻步态周期的上下文内容按照步态顺序。在两个数据集（最大的基于公共传感器的步态数据库OU-ISIR和我们实验室的新数据集）上的实验结果表明，我们的攻击模型可以在有限的尝试中绕过大多数受害者的模型。具体来说，我们可以通过模仿在5次尝试中折中20％–80％的用户。相反，通过部署我们的多周期防御模型，大大降低了攻击者的成功率。