Lightweight 3D-StudentNet for defending against face replay attacks.

Biometric face and lip-reading systems are susceptible to face replay attacks. Where the intruder presents a recorded video of a legitimate user or presents printed photos to gain system access without authorization. Consequently, liveness detection becomes essential to confirm whether the person in camera view is real rather than fake replays. This study aims to detect such printed photo attacks and video or photo replay attacks on high-resolution screens. The main objective of this research is to develop a lightweight 3D-DNN model that considers both spatial and temporal features to distinguish between real and attack videos. To achieve this objective, a lightweight 3D-StudentNet face replay attack defense system is proposed leveraging 3D-ArrowNet deep neural network and knowledge distillation. The system captures dynamic spatio-temporal features from five video frames captured by an RGB camera. Considerable experimentation is conducted using the Replay-Attack and Replay-Mobile benchmark datasets. Experimental results demonstrate that the proposed 3D-ArrowNet achieves state-of-the-art performance and transfers its knowledge successfully to a lightweight 3D-StudentNet with fewer network parameters. Thus, 3D-StudentNet can supplant existing 2D-DNN architectures utilized for replay attack defense systems, which capture only spatial features. [ABSTRACT FROM AUTHOR]