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:近年来,确认伪造视频一直是一个挑战。Deepfake分类器现在可以可靠地预测视频帧是否被篡改。然而,它们的性能与用于训练的数据集和分析人员的计算能力密切相关。我们提出了一种在训练高质量人脸图像的最先进的生成对抗网络(GAN)的潜空间中操作的Deepfake分类方法。所提出的方法利用StyleGAN的潜空间结构来学习轻量级分类模型。在标准数据集上的实验结果表明,所提出的方法优于其他最先进的Deepfake分类方法。据我们了解,这是首个展示利用StyleGAN潜空间进行Deepfake分类的研究。结合最近关于解释和操纵潜空间的其他研究,我们相信所提出的方法可以帮助开发基于可解释的高级脸部属性的强健Deepfake分类方法。