Face Anti-spoofing with a Noise-Attention Network Using Color-Channel Difference Images

Abstract

The wide deployment of face recognition systems has raised serious concern on its security level. One of the most prominent threats is the presentation attack, which attempts to fool the system in the absence of the real person. In this article, we propose a way of identifying the spoof face by using spoofing noise. Since the spoofing noise is brought into the spoof image when an adversary uses some tricks to fool the face recognition system, it consists of imagining device noise and is affected by the spoofing environments. We think it is a clue against fake face. We first address how to use color channel difference images to enhance the spoofing noise, and then introduce a self-adapting attention framework named Noise-Attention Network to learn the end-to-end spoofing-features. Experiments on benchmarks including CASIA-FASD, MSU-MFSD and Idiap Replay-Attack demonstrate the effectiveness of the proposed method. It can yield results comparable with other current methods but has better robustness.