The classification of forged videos has been a challenge for the past few years. Deepfake classifiers can now reliably predict whether or not video frames have been tampered with. However, their performance is tied to both the dataset used for training and the analyst's computational power. We propose a deepfake classification method that operates in the latent space of a state-of-the-art generative adversarial network (GAN) trained on high-quality face images. The proposed method leverages the structure of the latent space of StyleGAN to learn a lightweight classification model. Experimental results on a standard dataset reveal that the proposed approach outperforms other state-of-the-art deepfake classification methods. To the best of our knowledge, this is the first study showing the interest of the latent space of StyleGAN for deepfake classification. Combined with other recent studies on the interpretation and manipulation of this latent space, we believe that the proposed approach can help in developing robust deepfake classification methods based on interpretable high-level properties of face images.

翻译：在过去几年中，伪造视频的分类一直是一项挑战。深度伪造分类器现在能够可靠地预测视频帧是否被篡改。然而，其性能既受限于训练所用的数据集，也受限于分析人员的计算能力。我们提出了一种深度伪造分类方法，该方法在基于高质量人脸图像训练的最先进生成对抗网络（GAN）的潜在空间中运行。所提出的方法利用StyleGAN潜在空间的结构来学习一个轻量级分类模型。在标准数据集上的实验结果表明，该方法优于其他最先进的深度伪造分类方法。据我们所知，这是首次展示StyleGAN潜在空间在深度伪造分类中价值的研究。结合其他关于该潜在空间解释与操控的最新研究，我们相信该提出的方法有助于基于人脸图像可解释高层属性开发鲁棒的深度伪造分类方法。