Security-Preserving Live 3D Video Surveillance
Security-Preserving Live 3D Video Surveillance
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DOI：
10.1145/3587819.3590975
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Zhongze Tang;Huy Phan;Xianglong Feng;Bo Yuan;Yao Liu;Sheng Wei
中科院分区：
文献类型：
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作者：
Zhongze Tang;Huy Phan;Xianglong Feng;Bo Yuan;Yao Liu;Sheng Wei
随着3D深度摄像头在消费市场的普及，3D视频监控已成为安防监控的新趋势。在实现更有效的监控功能的同时，捕获的更细粒度的 3D 视频将引发新的安全问题，而现有研究尚未解决这些问题。本文探讨了实时 3D 监控视频触发生物识别相关攻击（例如面部 ID 欺骗）的安全影响。我们证明，监控视频中呈现的 3D 人脸模型可以有效地破坏最先进的人脸身份验证系统。然后，为了防御此类面部欺骗攻击，我们建议在暴露给潜在对手之前，主动且善意地向监控视频实时注入对抗性扰动。这种动态生成的扰动可以防止人脸模型被利用来绕过基于深度学习的人脸身份验证，同时保持 3D 视频监控所需的质量和功能。我们在 RGB-D 数据集和 3D 视频数据集上评估了所提出的扰动生成方法，这证明了其有效的安全保护、低质量退化和实时性能。
3D video surveillance has become the new trend in security monitoring with the popularity of 3D depth cameras in the consumer market. While enabling more fruitful surveillance features, the finer-grained 3D videos being captured would raise new security concerns that have not been addressed by existing research. This paper explores the security implications of live 3D surveillance videos in triggering biometrics-related attacks, such as face ID spoofing. We demonstrate that the state-of-the-art face authentication systems can be effectively compromised by the 3D face models presented in the surveillance video. Then, to defend against such face spoofing attacks, we propose to proactively and benignly inject adversarial perturbations to the surveillance video in real time, prior to the exposure to potential adversaries. Such dynamically generated perturbations can prevent the face models from being exploited to bypass deep learning-based face authentications while maintaining the required quality and functionality of the 3D video surveillance. We evaluate the proposed perturbation generation approach on both an RGB-D dataset and a 3D video dataset, which justifies its effective security protection, low quality degradation, and real-time performance.