Beyond the Pixel World: A Novel Acoustic-Based Face Anti-Spoofing System for Smartphones

Abstract

2D face presentation attacks are one of the most notorious and pervasive face spoofing types, which have caused pressing security issues to facial authentication systems. While RGB-based face anti-spoofing (FAS) models have proven to counter the face spoofing attack effectively, most existing FAS models suffer from the overfitting problem (i.e., lack generalization capability to data collected from an unseen environment). Recently, many models have been devoted to capturing auxiliary information (<i>e.g</i>., depth and infrared maps) to achieve a more robust face liveness detection performance. However, these methods require expensive sensors and cost extra hardware to capture the specific modality information, limiting their applications in practical scenarios. To tackle these problems, we devise a novel and cost-effective FAS system based on the acoustic modality, named Echo-FAS, which employs the crafted acoustic signal as the probe to perform face liveness detection. We first propose to build a large-scale, high-diversity, and acoustic-based FAS database, Echo-Spoof. Then, based upon Echo-Spoof, we propose designing a novel two-branch framework that combines the global and local frequency clues of input signals to distinguish inputs, live vs. spoofing faces accurately. The devised Echo-FAS comprises the following three merits: (1) It only needs one available speaker and microphone as sensors while not requiring any expensive hardware; (2) It can successfully capture the 3D geometrical information of input queries and achieve a remarkable face anti-spoofing performance; and (3) It can be handily allied with other RGB-based FAS models to mitigate the overfitting problem in the RGB modality and make the FAS model more accurate and robust. Our proposed Echo-FAS provides new insights regarding the development of FAS systems for mobile devices.