论文标题
面部操纵的深刻检测
Deep Detection for Face Manipulation
论文作者
论文摘要
由于近年来基于深度学习的面部操纵技术的巨大进步，将真实面孔与视觉上现实的假货配音区分开来变得越来越具有挑战性。在本文中，我们引入了一种深度学习方法来检测面部操纵。它由两个阶段组成：特征提取和二进制分类。为了更好地区分假面的面孔，我们在第一阶段求助于三胞胎损失功能。然后，我们设计了一个简单的线性分类网络，以用真实的/假面弥合学习的对比功能。公共基准数据集的实验结果证明了这种方法的有效性，并表明它在大多数情况下产生的性能比最先进的技术更好。
It has become increasingly challenging to distinguish real faces from their visually realistic fake counterparts, due to the great advances of deep learning based face manipulation techniques in recent years. In this paper, we introduce a deep learning method to detect face manipulation. It consists of two stages: feature extraction and binary classification. To better distinguish fake faces from real faces, we resort to the triplet loss function in the first stage. We then design a simple linear classification network to bridge the learned contrastive features with the real/fake faces. Experimental results on public benchmark datasets demonstrate the effectiveness of this method, and show that it generates better performance than state-of-the-art techniques in most cases.
