A Dataless FaceSwap Detection Approach Using Synthetic Images
This addresses the need for less biased and data-efficient deepfake detection tools, though it is incremental as it builds on existing synthetic data approaches.
The paper tackles the problem of detecting deepfake face swaps by proposing a method that uses synthetic images from StyleGAN3 instead of real data, achieving performance comparable to traditional methods with better generalization and reduced ethnic bias.
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.