DeepFake Detection
129 papers with code • 5 benchmarks • 18 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
Benchmarks
These leaderboards are used to track progress in DeepFake Detection
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Libraries
Use these libraries to find DeepFake Detection models and implementationsDatasets
FaceForensics++
Celeb-DF
DFDC
FaceForensics
WildDeepfake
FakeAVCeleb
DeeperForensics-1.0
KoDF
Most implemented papers
FaceForensics++: Learning to Detect Manipulated Facial Images
In particular, the benchmark is based on DeepFakes, Face2Face, FaceSwap and NeuralTextures as prominent representatives for facial manipulations at random compression level and size.
Taming Transformers for High-Resolution Image Synthesis
CompVis/taming-transformers
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CVPR 2021
We demonstrate how combining the effectiveness of the inductive bias of CNNs with the expressivity of transformers enables them to model and thereby synthesize high-resolution images.
MesoNet: a Compact Facial Video Forgery Detection Network
DariusAf/MesoNet
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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.
Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information.
The DeepFake Detection Challenge (DFDC) Dataset
In addition to Deepfakes, a variety of GAN-based face swapping methods have also been published with accompanying code.
Unmasking DeepFakes with simple Features
In this work, we present a simple way to detect such fake face images - so-called DeepFakes.
Face X-ray for More General Face Forgery Detection
For this reason, face X-ray provides an effective way for detecting forgery generated by most existing face manipulation algorithms.
Combining EfficientNet and Vision Transformers for Video Deepfake Detection
davide-coccomini/Combining-EfficientNet-and-Vision-Transformersfor-Video-Deepfake-Detection
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Traditionally, Convolutional Neural Networks (CNNs) have been used to perform video deepfake detection, with the best results obtained using methods based on EfficientNet B7.
Analyzing Fairness in Deepfake Detection With Massively Annotated Databases
In this work, we investigate factors causing biased detection in public Deepfake datasets by (a) creating large-scale demographic and non-demographic attribute annotations with 47 different attributes for five popular Deepfake datasets and (b) comprehensively analysing attributes resulting in AI-bias of three state-of-the-art Deepfake detection backbone models on these datasets.
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
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.
