Convolutional Neural Networks to Protect Against Spoofing Attacks on Biometric Face Authentication

Modern technologies of authentication and authorization of access play a significant role in ensuring the protection of information in various practical applications. We consider the most convenient and used in modern mobile gadgets face authentication, ie when the primary information to provide access are certain features of biometric images of the user’s face. Most of the systems use intelligent processing of biometric images, in particular, artificial intelligence technology and deep learning. But at the same time, as always in cybersecurity, technologies for violating biometric authentication are being studied and researched. In particular, to date, the most common attack is substitution (spoofing), ie when attackers use pre-recorded biometric images to gain unauthorized access to critical information. For example, this could be a photo and/or video image of a person used to unlock their smartphone. Protection against such attacks is very difficult, because it involves the development and study of technologies for detecting signs of life. The most promising in this direction are artificial intelligence techniques, in particular, convolutional neural networks (CNN). This is the practical application of intelligent processing of biometric images and is studied in this article. We review various CNN settings and configurations and experimentally investigate their effect on the effectiveness of signs of life detection. For this purpose, success and failure indicators of the first and second kind are used, which are estimated by the values of cross entropy. These are reliable and reproducible indicators that characterize the effectiveness of protection against spoofing attacks on biometric authentication on the face. The world-famous TensorFlow and OpenCV libraries are used for field experiments, photos and videos of various users are used as source data, including Replay-Attack Database from Idiap Research Institute.