22 papers with code • 3 benchmarks • 7 datasets
The free access to large-scale public databases, together with the fast progress of deep learning techniques, in particular Generative Adversarial Networks, have led to the generation of very realistic fake content with its corresponding implications towards society in this era of fake news.
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
This paper presents a method to automatically and efficiently detect face tampering in videos, and particularly focuses on two recent techniques used to generate hyper-realistic forged videos: Deepfake and Face2Face.
AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information.
The creation and the manipulation of facial appearance via deep generative approaches, known as DeepFake, have achieved significant progress and promoted a wide range of benign and malicious applications.
In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation techniques.
Ranked #1 on DeepFake Detection on FaceForensics++
For this reason, face X-ray provides an effective way for detecting forgery generated by most existing face manipulation algorithms.
WildDeepfake is a small dataset that can be used, in addition to existing datasets, to develop and test the effectiveness of deepfake detectors against real-world deepfakes.