PulseEdit: Editing Physiological Signal in Facial Videos for Privacy Protection

Recent studies have shown that physiological signals can be remotely captured from human faces using a portable color camera under ambient light. This technology, namely remote photoplethysmography (rPPG), can be used to collect users' physiological status who are sitting in front of a camera, which may raise physiological privacy issues. 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 to 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 some 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.

Angle of attack prediction using recurrent neural networks in flight conditions with faulty sensors in the case of F-16 fighter jet

The angle of attack (AOA) is one of the critical parameters in a fixed-wing aircraft because all aerodynamic forces are functions of the AOA. Most methods for estimation of the AOA do not provide information on the method’s performance in the presence of noise, faulty total velocity measurement, and faulty pitch rate measurement. This paper investigates data-driven modeling of the F-16 fighter jet and AOA prediction in flight conditions with faulty sensor measurements using recurrent neural networks (RNNs). The F-16 fighter jet is modeled in several architectures: simpleRNN (sRNN), long-short-term memory (LSTM), gated recurrent unit (GRU), and the combinations LSTM-GRU, sRNN-GRU, and sRNN-LSTM. The developed models are tested by their performance to predict the AOA of the F-16 fighter jet in flight conditions with faulty sensor measurements: faulty total velocity measurement, faulty pitch rate and total velocity measurement, and faulty AOA measurement. We show the model obtained using sRNN trained with the adaptive momentum estimation algorithm (Adam) produces more exact predictions during faulty total velocity measurement and faulty total velocity and pitch rate measurement but fails to perform well during faulty AOA measurement. The sRNN-GRU combinations with the GRU layer closer to the output layer performed better than all the other networks. When using this architecture, the correlation and mean squared error (MSE) between the true (real) value and the predicted value during faulty AOA measurement increased by 0.12 correlation value and the MSE decreased by 4.3 degrees if one uses only sRNN. In the sRNN-GRU combined architecture, moving the GRU closer to the output layer produced a model with better predicted values.