Enhancing Mobile Privacy and Security: A Face Skin Patch-Based Anti-Spoofing Approach
원문 초록
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.
함께 살펴볼 논문
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FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection
DiffFAS: Face Anti-Spoofing via Generative Diffusion Models
A visualization method for data domain changes in CNN networks and the optimization method for selecting thresholds in classification tasks
Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues
Leveraging Intermediate Features of Vision Transformer for Face Anti-Spoofing
토론
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