  Deepfake poses a serious threat to the reliability of judicial evidence and
intellectual property protection. In spite of an urgent need for Deepfake
identification, existing pixel-level detection methods are increasingly unable
to resist the growing realism of fake videos and lack generalization. In this
paper, we propose a scheme to expose Deepfake through faint signals hidden in
face videos. This scheme extracts two types of minute information hidden
between face pixels-photoplethysmography (PPG) features and auto-regressive
(AR) features, which are used as the basis for forensics in the temporal and
spatial domains, respectively. According to the principle of PPG, tracking the
absorption of light by blood cells allows remote estimation of the temporal
domains heart rate (HR) of face video, and irregular HR fluctuations can be
seen as traces of tampering. On the other hand, AR coefficients are able to
reflect the inter-pixel correlation, and can also reflect the traces of
smoothing caused by up-sampling in the process of generating fake faces.
Furthermore, the scheme combines asymmetric convolution block (ACBlock)-based
improved densely connected networks (DenseNets) to achieve face video
authenticity forensics. Its asymmetric convolutional structure enhances the
robustness of network to the input feature image upside-down and left-right
flipping, so that the sequence of feature stitching does not affect detection
results. Simulation results show that our proposed scheme provides more
accurate authenticity detection results on multiple deep forgery datasets and
has better generalization compared to the benchmark strategy.
