Deep Pixelwise Binary Supervision for Face Presentation Attack Detection Face recognition has evolved as a prominent biometric authentication modality. However, vulnerability to presentation attacks curtails its reliable deployment. Automatic detection of presentation attacks is essential for secure use of face recognition technology in unattended scenarios. In this work, we introduce a Convolutional Neural Network (CNN) based framework for presentation attack detection, with deep pixelwise supervision. The framework uses only frame level information making it suitable for deployment in smart devices with minimal computational and time overhead. We demonstrate the effectiveness of the proposed approach in public datasets for both intra as well as crossdataset experiments. The proposed approach achieves an HTER of 0% in Replay Mobile dataset and an ACER of 0.42% in Protocol1 of OULU dataset outperforming state of the art methods.

Deceiving the Protector: Fooling Face Presentation Attack Detection Algorithms Face recognition systems are vulnerable to presentation attacks such as replay and 3D masks. In the literature, several presentation attack detection (PAD) algorithms are developed to address this problem. However, for the first time in the literature, this paper showcases that it is possible to fool the PAD algorithms using adversarial perturbations. The proposed perturbation approach attacks the presentation attack detection algorithms at the PAD feature level via transformation of features from one class (attack class) to another (real class). The PAD feature tampering network utilizes convolutional autoencoder to learn the perturbations. The proposed algorithm is evaluated with respect to CNN and local binary pattern (LBP) based PAD algorithms. Experiments on three databases, Replay, SMAD, and Face Morph, showcase that the proposed approach increases the equal error rate of PAD algorithms by at least two times. For instance, on the SMAD database, PAD equal error rate (EER) of 20.1% is increased to 55.7% after attacking the PAD algorithm.