论文标题
深果如何移动？ DeepFake源检测的运动放大倍率
How Do Deepfakes Move? Motion Magnification for Deepfake Source Detection
论文作者
论文摘要
随着深层生成模型的扩散，每天的质量和数量都在提高。但是，原始视频中有微妙的真实性信号，而不是由Sota Gans复制。我们通过放大运动与建立广义的深泡源检测器来对比深击和真实视频中的运动。面部中的肌肉运动在其生成残留物中反映出每个不同的生成模型的解释。我们的方法通过结合深层和传统的运动放大倍率来利用真实运动和放大的GAN指纹之间的差异，以检测视频是否是假的及其源发电机。评估我们在两个多源数据集上的方法，我们获得了97.17％和94.03％的视频源检测。我们将与先前的DeepFake源检测器和其他复杂体系结构进行比较。我们还分析了放大量，相位提取窗口，骨干网络体系结构，样本计数和样本长度的重要性。最后，我们报告了不同肤色评估偏见的结果。
With the proliferation of deep generative models, deepfakes are improving in quality and quantity everyday. However, there are subtle authenticity signals in pristine videos, not replicated by SOTA GANs. We contrast the movement in deepfakes and authentic videos by motion magnification towards building a generalized deepfake source detector. The sub-muscular motion in faces has different interpretations per different generative models which is reflected in their generative residue. Our approach exploits the difference between real motion and the amplified GAN fingerprints, by combining deep and traditional motion magnification, to detect whether a video is fake and its source generator if so. Evaluating our approach on two multi-source datasets, we obtain 97.17% and 94.03% for video source detection. We compare against the prior deepfake source detector and other complex architectures. We also analyze the importance of magnification amount, phase extraction window, backbone network architecture, sample counts, and sample lengths. Finally, we report our results for different skin tones to assess the bias.
