Deepfakes is a branch of malicious techniques that transplant a target face
to the original one in videos, resulting in serious problems such as
infringement of copyright, confusion of information, or even public panic.
Previous efforts for Deepfakes videos detection mainly focused on appearance
features, which have a risk of being bypassed by sophisticated manipulation,
also resulting in high model complexity and sensitiveness to noise. Besides,
how to mine the temporal features of manipulated videos and exploit them is
still an open question. We propose an efficient and robust framework named
LRNet for detecting Deepfakes videos through temporal modeling on precise
geometric features. A novel calibration module is devised to enhance the
precision of geometric features, making it more discriminative, and a
two-stream Recurrent Neural Network (RNN) is constructed for sufficient
exploitation of temporal features. Compared to previous methods, our proposed
method is lighter-weighted and easier to train. Moreover, our method has shown
robustness in detecting highly compressed or noise corrupted videos. Our model
achieved 0.999 AUC on FaceForensics++ dataset. Meanwhile, it has a graceful
decline in performance (-0.042 AUC) when faced with highly compressed videos.

LRNet 是一种有效且鲁棒的检测 Deepfakes 视频的框架，通过对精确几何特征的时间建模来挖掘深度伪造视频的时间特征，并对其进行有效利用，相较于以往的方法更轻量级且更易于训练，在高度压缩或噪声受损的情况下依然具有鲁棒性。