Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era

Authors: 

Changjiang Li, Pennsylvania State University and Zhejiang University; Li Wang, Shandong University; Shouling Ji and Xuhong Zhang, Zhejiang University; Zhaohan Xi, Pennsylvania State University; Shanqing Guo, Shandong University; Ting Wang, Pennsylvania State University

Abstract: 

Facial Liveness Verification (FLV) is widely used for identity authentication in many security-sensitive domains and offered as Platform-as-a-Service (PaaS) by leading cloud vendors. Yet, with the rapid advances in synthetic media techniques (e.g., deepfake), the security of FLV is facing unprecedented challenges, about which little is known thus far.

To bridge this gap, in this paper, we conduct the first systematic study on the security of FLV in real-world settings. Specifically, we present LiveBugger, a new deepfake-powered attack framework that enables customizable, automated security evaluation of FLV. Leveraging LiveBugger, we perform a comprehensive empirical assessment of representative FLV platforms, leading to a set of interesting findings. For instance, most FLV APIs do not use anti-deepfake detection; even for those with such defenses, their effectiveness is concerning (e.g., it may detect high-quality synthesized videos but fail to detect low-quality ones). We then conduct an in-depth analysis of the factors impacting the attack performance of LiveBugger: a) the bias (e.g., gender or race) in FLV can be exploited to select victims; b) adversarial training makes deepfake more effective to bypass FLV; c) the input quality has a varying influence on different deepfake techniques to bypass FLV. Based on these findings, we propose a customized, two-stage approach that can boost the attack success rate by up to 70%. Further, we run proof-of-concept attacks on several representative applications of FLV (i.e., the clients of FLV APIs) to illustrate the practical implications: due to the vulnerability of the APIs, many downstream applications are vulnerable to deepfake. Finally, we discuss potential countermeasures to improve the security of FLV. Our findings have been confirmed by the corresponding vendors.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

BibTeX
@inproceedings {279994,
title = {Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era},
booktitle = {31st USENIX Security Symposium (USENIX Security 22)},
year = {2022},
address = {Boston, MA},
url = {https://www.usenix.org/conference/usenixsecurity22/presentation/li-changjiang},
publisher = {USENIX Association},
month = aug,
}