A document by Qiang Zhu . Click on the document to view its contents.

Recent studies have shown that physiological signals such as heart beat and breathing can be remotely captured from human faces using a regular color camera under ambient light. This technology, referred to as remote photoplethysmography (rPPG), can be used to collect the physiological status of users who are in front of a camera, which may raise privacy concerns. To avoid the privacy abuse of the rPPG technology, this paper develops PulseEdit, a novel and efficient algorithm that can edit the physiological signals in facial videos without affecting visual appearance and thus protect the user’s physiological signal from disclosure. PulseEdit can either remove the trace of the physiological signal in a video or transform the video to contain a target physiological signal chosen by a user. Experimental results show that PulseEdit can effectively edit physiological signals in facial videos and prevent heart rate measurement based on rPPG. It is possible to utilize PulseEdit in adversarial scenarios against rPPG-based visual security algorithms. We present analyses on the performance of PulseEdit against rPPG-based liveness detection and rPPG-based deepfake detection, and demonstrate its ability to circumvent these visual security algorithms and its important role in supporting the design of attack-resilient systems.

Camera-based remote photoplethysmography (rPPG) technology has shown a promising future in contact-free cardiac and other smart health applications. The rPPG technology typically requires facial videos as a source input, which may lead to identity-privacy concerns. Facial videos are sensitive and contain subjects’ identifiable appearance features. Coupled with the health information potentially revealed by rPPG techniques, the compounding sensitivity has been a major obstacle to encouraging the sharing of facial rPPG video datasets in the research community to foster the advancement of the field. This paper investigates a suite of anonymization transforms that remove the identifiable appearance features in facial videos and retain the physiological signals for rPPG analysis. After the transformation, the facial videos are de-identified and may be shared in public with little risk of identity-privacy leakage. The proposed algorithm offers tunable options to balance the physiological fidelity and the identity-protecting strength to meet different levels of privacy requirements. A human subject study has been carried out to understand – both qualitatively and quantitatively – the perceived strength and efficacy of privacy protection by these anonymization techniques in de-identifying the facial videos and maintaining the physiological signals.