DepthFake: Spoofing 3D Face Authentication with a 2D Photo

Abstract

Face authentication has been widely used in access control, and the latest 3D face authentication systems employ 3D liveness detection techniques to cope with the photo replay attacks, whereby an attacker uses a 2D photo to bypass the authentication. In this paper, we analyze the security of 3D liveness detection systems that utilize structured light depth cameras and discover a new attack surface against 3D face authentication systems. We propose DepthFake attacks that can spoof a 3D face authentication using only one single 2D photo. To achieve this goal, DepthFake first estimates the 3D depth information of a target victim’s face from his 2D photo. Then, DepthFake projects the carefully-crafted scatter patterns embedded with the face depth information, in order to empower the 2D photo with 3D authentication properties. We overcome a collection of practical challenges, e.g., depth estimation errors from 2D photos, depth images forgery based on structured light, the alignment of the RGB image and depth images for a face, and implemented DepthFake in laboratory setups. We validated DepthFake on 3 commercial face authentication systems (i.e., Tencent Cloud, Baidu Cloud, and 3DiVi) and one commercial access control device. The results over 50 users demonstrate that DepthFake achieves an overall Depth attack success rate of 79.4% and RGB-D attack success rate of 59.4% in the real world.