computer-vision-5
DeepFake Detection
3260 papers • 126 benchmarks • 313 datasets
DeepFake Detection is the task of detecting fake videos or images that have been generated using deep learning techniques. Deepfakes are created by using machine learning algorithms to manipulate or replace parts of an original video or image, such as the face of a person. The goal of deepfake detection is to identify such manipulations and distinguish them from real videos or images. Description source: DeepFakes: a New Threat to Face Recognition? Assessment and Detection Image source: DeepFakes: a New Threat to Face Recognition? Assessment and Detection
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in deepfake-detection-15
Trend
Dataset
Best Model
Actions
FakeAVCeleb
FakeAVCeleb
FaceForensics++
FaceForensics++
DFDC
DFDC
Libraries
iUse these libraries to find deepfake-detection-15 models and implementations
3 papers 42
Datasets
Subtasks
Most implemented papers
FaceForensics++: Learning to Detect Manipulated Facial Images
M. Nießner, C. Riess, Justus Thies, D. Cozzolino, L. Verdoliva, Andreas Rössler•Thu Jan 24 2019
This paper proposes an automated benchmark for facial manipulation detection, and shows that the use of additional domain-specific knowledge improves forgery detection to unprecedented accuracy, even in the presence of strong compression, and clearly outperforms human observers.
2677
