Sensitive Information of Deep Learning Based Face Anti-spoofing Algorithms

摘要

Face anti-spoofing based on deep learning achieved good accuracy recently. However, deep learning model has no explicit mathematical presentation. Therefore, it is not clear about how the model works effectively. In this paper, we estimate the regions in face image, which are sensitive in deep learning based anti-spoofing algorithms. We first generate the adversarial examples from two different gradient-based methods. Then we analyze the distribution of the gradient and perturbations on the adversarial examples. And next we obtain the sensitive regions and evaluate the contribution of these regions to classification performance. By analyzing the sensitive regions, it could be observed that the CNN based anti-spoofing algorithms are sensitive to rich detailed regions and illumination. These observations are helpful to design an effective face anti-spoofing algorithm.