Detecting Manipulated Facial Videos: A Time Series Solution
Zhewei Zhang, Can Mal, Bowen Ding, Meilin Gao · 2021
We propose a new method to expose fake videos based on a time series solution. The method is based on bidirectional long short-term memory (Bi-LSTM) backbone architecture with two different types of features: Face-Alignment and Dense-Face-Alignment, in which both of them are physiological signals that can be distinguished between fake and original videos. We choose 68 landmark points as the feature of Face-Alignment and Pose Adaptive Feature (PAF) for Dense-Face-Alignment. Based on these two facial features, we designed two deep networks. In addition, we optimize our network by adding an attention mechanism that improves detection precision. Our method is tested over benchmarks of Face Forensics/Face Forensics++ dataset and show a promising performance on inference speed while maintaining accuracy with state-of art solutions that deal against DeepFake.