增强移动隐私与安全：一种基于面部皮肤块的防欺骗方法
Enhancing Mobile Privacy and Security: A Face Skin Patch-Based Anti-Spoofing Approach
AI总结：
本文针对人脸防欺骗中隐私泄露和加解密耗时问题，提出基于纯面部皮肤块图像的防欺骗算法，无需加解密，并在多个公开数据集上验证了其在准确性和速度上的优势。
摘要：
由于人脸识别系统（FRS）因其便利性和高准确性被广泛应用于门禁控制和移动支付等领域，人脸识别的安全性也受到高度重视。用于人脸识别的人脸防欺骗系统（FAS）是增强人脸识别系统安全性的重要组成部分。传统FAS使用包含身份信息的图像来检测欺骗痕迹，然而这些图像在传输和存储过程中存在隐私泄露的风险。此外，与FAS模型的推理时间相比，对这些隐私敏感数据进行加密和解密耗时过长。为解决上述问题，我们提出了一种基于面部皮肤块的人脸防欺骗算法，利用纯面部皮肤块图像作为输入，这些图像不包含隐私信息，因此无需加密或解密。我们在多个公开数据集上进行了实验，结果证明我们的算法在准确性和速度方面均表现出优越性。
英文摘要：
As Facial Recognition System(FRS) is widely applied in areas such as access control and mobile payments due to its convenience and high accuracy. The security of facial recognition is also highly regarded. The Face anti-spoofing system(FAS) for face recognition is an important component used to enhance the security of face recognition systems. Traditional FAS used images containing identity information to detect spoofing traces, however there is a risk of privacy leakage during the transmission and storage of these images. Besides, the encryption and decryption of these privacy-sensitive data takes too long compared to inference time by FAS model. To address the above issues, we propose a face anti-spoofing algorithm based on facial skin patches leveraging pure facial skin patch images as input, which contain no privacy information, no encryption or decryption is needed for these images. We conduct experiments on several public datasets, the results prove that our algorithm has demonstrated superiority in both accuracy and speed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
