Abstract
Face authentication systems are becoming more and more prevalent, but it has an intrinsic vulnerability against the media-based face forgery (MFF) where adversaries display photos or videos containing victims’ faces to deceive face authentication systems. Liveness detection is an important defense technique to prevent such attacks. In this paper, we propose a practical and effective liveness detection mechanism to protect the face authentication system against the MFF-based attacks. Our approach send the challenge to the user in random and the camera capture the response as a video. The Local Binary Pattern (LBP) is a widely used descriptor in texture analysis due to its efficiency. We utilize \(\delta \)-LBP, a LBP variant, to detect the expression frame from the video. Additionally, We improve the original LBP by using proper sampling radius in different subareas of a facial image and apply the approach in extracting the facial texture feature from the expression frame as a histogram. Our method detects the MFF-based attacks by measuring the consistency between the LBP histogram and the real facial texture feature. To demonstrate its effectiveness, We collect real-world photo data and video data from both legitimate authentication requests and the MFF-based attacks. The experiment results show that it can detect the MFF-based attacks with an accuracy of 96.45%.
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Acknowledgment
The work was supported in part by NSFC under Grant 61802289, 61671013. We thank those anonymous reviewers for their insightful comments. We also want to thank those participants for providing their face data in our experiment.
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Yang, Z., Li, Q., Li, Y., Wang, Z. (2019). A LBP Texture Analysis Based Liveness Detection for Face Authentication. In: Chen, X., Huang, X., Zhang, J. (eds) Machine Learning for Cyber Security. ML4CS 2019. Lecture Notes in Computer Science(), vol 11806. Springer, Cham. https://doi.org/10.1007/978-3-030-30619-9_3
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DOI: https://doi.org/10.1007/978-3-030-30619-9_3
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