Deep Face Forgery Detection

• URL: http://arxiv.org/abs/2004.11804v1
• Date: Mon, 6 Apr 2020 11:13:04 GMT
• Title: Deep Face Forgery Detection
• Authors: Nika Dogonadze, Jana Obernosterer, Ji Hou
• Abstract summary: This paper describes an approach for various tampering scenarios. We propose to use transfer learning from face recognition task to improve tampering detection. We evaluate both approaches on the public FaceForensics benchmark, achieving state of the art accuracy.
• Score: 6.230751621285322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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