Deep Face Forgery Detection
This addresses the need for reliable detection of facial video forgeries, which is crucial for security and media integrity, though it appears incremental as it builds on existing methods and benchmarks.
The paper tackles the problem of detecting deepfake videos by modeling it as a per-frame binary classification task, achieving state-of-the-art accuracy on the FaceForensics benchmark through transfer learning from face recognition and leveraging neighboring frames in low-resolution settings.
Rapid progress in deep learning is continuously making it easier and cheaper to generate video forgeries. Hence, it becomes very important to have a reliable way of detecting these forgeries. This paper describes such an approach for various tampering scenarios. The problem is modelled as a per-frame binary classification task. We propose to use transfer learning from face recognition task to improve tampering detection on many different facial manipulation scenarios. Furthermore, in low resolution settings, where single frame detection performs poorly, we try to make use of neighboring frames for middle frame classification. We evaluate both approaches on the public FaceForensics benchmark, achieving state of the art accuracy.