论文标题
使用合成图像的数据脸部faceSWAP检测方法
A Dataless FaceSwap Detection Approach Using Synthetic Images
论文作者
论文摘要
在过去的几年中，用于创建“深击”的面部交换技术已经大大发展，现在使我们能够创建现实的面部操作。当前的深度学习算法以检测深烟味已显示出令人鼓舞的结果，但是它们需要大量的培训数据，并且由于我们表明它们对特定种族有偏见。我们提出了一种深层检测方法，该方法通过使用stylegan3的合成生成的数据来消除对任何真实数据的需求。这不仅与使用真实数据的传统培训方法相提并论，而且在使用少量实际数据进行填充时，它显示出更好的概括功能。此外，这还减少了面部图像数据集创造的偏见，这些数据集可能具有特定种族的数据稀少。
Face swapping technology used to create "Deepfakes" has advanced significantly over the past few years and now enables us to create realistic facial manipulations. Current deep learning algorithms to detect deepfakes have shown promising results, however, they require large amounts of training data, and as we show they are biased towards a particular ethnicity. We propose a deepfake detection methodology that eliminates the need for any real data by making use of synthetically generated data using StyleGAN3. This not only performs at par with the traditional training methodology of using real data but it shows better generalization capabilities when finetuned with a small amount of real data. Furthermore, this also reduces biases created by facial image datasets that might have sparse data from particular ethnicities.
