论文标题
深脸伪造
Deep Face Forgery Detection
论文作者
论文摘要
深度学习的快速进步是不断使产生视频伪造的更轻松和便宜的。因此，拥有一种可靠的方法来检测这些伪证，这变得非常重要。本文描述了针对各种篡改方案的这种方法。该问题被建模为人均二进制分类任务。我们建议使用从面部识别任务中进行转移学习，以改善许多不同面部操作方案的篡改检测。此外，在低分辨率设置中，单帧检测性能差，我们尝试利用相邻的帧进行中框架分类。我们评估了公众面孔基准测试的这两种方法，从而达到了技术的准确性。
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.
