Logo image
Back
Cross Signal Attacks Against Cardiovascular Authentication Systems
Journal article

Cross Signal Attacks Against Cardiovascular Authentication Systems

Bonan Zhang, Lin Li, Chao Chen, Ickjai Lee, Kyungmi Lee, Tianqing Zhu and Kok-Leong Ong
IEEE transactions on biometrics, behavior, and identity science, Vol.First online
11/06/2026
DOI: https://doi.org/10.1109/TBIOM.2026.3702142
Appears in  Recent Faculty of Science and Engineering Publications

Metrics

1 Record Views

Abstract

biometric ECG authentication diffusion generative models
ECG and PPG biometrics are emerging as robust alternatives to traditional authentication methods, particularly in scenarios requiring enhanced security, continuous authentication, and strong resilience against spoofing attacks. This paper investigates and validates the feasibility of launching adversarial attacks against cardiovascular biometric authentication systems by using diffusion models to generate ECG and PPG data that are difficult for authentication algorithms to distinguish from genuine signals. First, we developed a method using a diffusion model to synthesize fake biometric signals for user impersonation, achieving a high attack success rate against completely black-box authentication models. Secondly, we directly extracted users' rPPG signals from videos and assessed the potential for exploiting video data to compromise users' ECG-based authentication systems. Our experimental results show that across all algorithms, we achieved an average of 45% attack success rate within five attempts, thus validating the real threat posed by synthetic signals. This finding highlights that even without direct access to the cardiovascular signals used for authentication, an attacker can still launch highly successful attacks by synthesizing signals to deceive the verification system. Our experiments highlight potential vulnerabilities in current biometric systems and underscore the need to develop more secure and attack-resistant authentication technologies.

Details

Logo image