Temporal Sparse Adversarial Attack on Gait Recognition

摘要

Gait recognition has a broad application in social security due to its advantages in long-distance human identification. Despite the high accuracy of gait recognition systems, their adversarial robustness has not been explored. In this paper, we demonstrate that the state-of-the-art gait recognition model is vulnerable to adversarial attacks. A novel temporal sparse adversarial attack under a new defined distortion measurement is proposed. GAN-based architecture is employed to semantically generate adversarial high-quality gait silhouette. By sparsely substituting or inserting a few adversarial gait silhouettes, our proposed method can achieve a high attack success rate. The imperceptibility and the attacking success rate of the adversarial examples are well balanced. Experimental results show even only one-fortieth frames are attacked, the attack success rate still reaches 76.8%.

出版物
arXiv preprint arXiv:
何子文
何子文
在读博士、联合指导

主要从事人工智能安全、对抗样本等方面的研究。

王伟
王伟
副研究员、硕导

主要从事多媒体内容安全、人工智能安全、多模态内容分析与理解等方面的研究工作。

董晶
董晶
研究员、硕导

主要从事多媒体内容安全、人工智能安全、多模态内容分析与理解等方面的研究工作。详情访问:http://cripac.ia.ac.cn/people/jdong

谭铁牛
谭铁牛
研究员,博导

主要从事图像处理、计算机视觉和模式识别等相关领域的研究工作,目前的研究主要集中在生物特征识别、图像视频理解和信息内容安全等三个方向。