A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method.
Given a GAN-generated image, we insert the traces of a specific camera model into it and deceive state-of-the-art detectors into believing the image was acquired by that model.
In particular, the benchmark is based on DeepFakes, Face2Face, FaceSwap and NeuralTextures as prominent representatives for facial manipulations at random compression level and size.
Ranked #1 on DeepFake Detection on FaceForensics
We devise a learning-based forensic detector which adapts well to new domains, i. e., novel manipulation methods and can handle scenarios where only a handful of fake examples are available during training.
Research on the detection of face manipulations has been seriously hampered by the lack of adequate datasets.