论文标题
用公制学习检测深击
Detecting Deepfakes with Metric Learning
论文作者
论文摘要
随着几个面部交换应用的到来，例如FaceApp，Snapchat，Mixbooth，Faceblender等，数字媒体内容的真实性正挂在一个非常松散的线程上。在社交媒体平台上，视频经常以高压缩因素流传。在这项工作中，我们在高压缩方案中分析了深层分类的几种深度学习方法，并证明基于公制学习的建议方法可以非常有效地执行这种分类。每次视频的帧数量较少来评估其现实主义，使用三重态网络体系结构的公制学习方法被证明是富有成果的。它学会了增强嵌入向量的真实视频和假视频群之间的特征空间距离。我们验证了两个数据集上的方法，以分析不同环境中的行为。我们在高度压缩的神经纹理数据集上获得了Celeb-DF数据集的最新AUC分数99.2％，精度为90.71％。我们的方法在不可避免的数据压缩的社交媒体平台上特别有用。
With the arrival of several face-swapping applications such as FaceApp, SnapChat, MixBooth, FaceBlender and many more, the authenticity of digital media content is hanging on a very loose thread. On social media platforms, videos are widely circulated often at a high compression factor. In this work, we analyze several deep learning approaches in the context of deepfakes classification in high compression scenario and demonstrate that a proposed approach based on metric learning can be very effective in performing such a classification. Using less number of frames per video to assess its realism, the metric learning approach using a triplet network architecture proves to be fruitful. It learns to enhance the feature space distance between the cluster of real and fake videos embedding vectors. We validated our approaches on two datasets to analyze the behavior in different environments. We achieved a state-of-the-art AUC score of 99.2% on the Celeb-DF dataset and accuracy of 90.71% on a highly compressed Neural Texture dataset. Our approach is especially helpful on social media platforms where data compression is inevitable.
