A Dataless FaceSwap Detection Approach Using Synthetic Images
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.
Code (1)
Tasks
DeepFake DetectionFace SwappingSimilar Papers 제목 키워드 기반
Recurrent Convolutional Strategies for Face Manipulation Detection in Videos
The spread of misinformation through synthetically generated yet realistic images and videos has become a significant problem, calling for robust manipulation detection methods. Despite the predominant effort of detectin…
Face SwappingMisinformationDo Deepfake Detectors Work in Reality?
Deepfakes, particularly those involving faceswap-based manipulations, have sparked significant societal concern due to their increasing realism and potential for misuse. Despite rapid advancements in generative models, d…
DeepFake DetectionFace SwappingSuper-ResolutionFaceForensics++: Learning to Detect Manipulated Facial Images
The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. At best, this leads to a loss of trust in digital conte…
DeepFake DetectionFace SwappingFake Image DetectionImage GenerationFaceForensics++: Learning to Detect Manipulated Facial Images
The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. At best, this leads to a loss of trust in digital conte…
Face SwappingImage GenerationUnsupervised Label Refinement Improves Dataless Text Classification
Dataless text classification is capable of classifying documents into previously unseen labels by assigning a score to any document paired with a label description. While promising, it crucially relies on accurate descri…
ClassificationClusteringGeneral ClassificationText Classification+1