Deceptive Waves: Embedding Malicious Backdoors in PPG Authentication

Zeming Yao, Lin Li, Leo Yu Zhang, Fusen Guo, Chao Chen, Jun Zhang

Abstract: Recently, research interest has increasingly focused on the utilization of unobservable physiological signals as distinctive identifiers in biometric systems, which contributes to the enhancement of biometric authentication systems. Photoplethysmography (PPG) signals, favored for their ease of acquisition and integration with machine learning, generally exhibit robust protection against remote adversaries during authentication processes. However, the robustness of PPG signal models to backdoor attacks remains unexplored, and this powerful attack may pose a security threat to PPG-based biometric authentication systems due to its stealthiness. To the best of our knowledge, this paper first proposes a backdoor attack that targets PPG-based biometric authentication, which utilizes our elaborate waveform variations embedded in PPG signals as the backdoor. The compromised PPG-based authentication only behaves maliciously on the attacker-chosen inputs, while it behaves normally on clean inputs. We evaluate this backdoor attack on three popular datasets, showing that our attack successfully embeds and activates the backdoor in the PPG-based authentication. Experiment results on the state-of-the-art PPG-based authentication systems indicate that this first backdoor embedded in PPG signals poses a severe threat to PPG-based biometric authentication.