Abstract
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.