DeepFake Detection
177 papers with code • 8 benchmarks • 21 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
FakeAVCeleb
WildDeepfake
DeeperForensics-1.0
WaveFake
Most implemented papers
A Convolutional LSTM based Residual Network for Deepfake Video Detection
Also, they do not take advantage of the temporal information of the video.
DeepFakesON-Phys: DeepFakes Detection based on Heart Rate Estimation
This work introduces a novel DeepFake detection framework based on physiological measurement.
Neural Deepfake Detection with Factual Structure of Text
jmpu/deepfaketextdetection
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EMNLP 2020
To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text.
Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection
ahaliassos/lipforensics
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CVPR 2021
Extensive experiments show that this simple approach significantly surpasses the state-of-the-art in terms of generalisation to unseen manipulations and robustness to perturbations, as well as shed light on the factors responsible for its performance.
Learning Self-Consistency for Deepfake Detection
jtchen0528/PCL-I2G
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ICCV 2021
We propose a new method to detect deepfake images using the cue of the source feature inconsistency within the forged images.
WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection
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.
Towards Solving the DeepFake Problem : An Analysis on Improving DeepFake Detection using Dynamic Face Augmentation
In this paper, we focus on identifying the limitations and shortcomings of existing deepfake detection frameworks.
Deepfake Video Detection Using Convolutional Vision Transformer
erprogs/CViT
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In this work, we propose a Convolutional Vision Transformer for the detection of Deepfakes.
Countering Malicious DeepFakes: Survey, Battleground, and Horizon
clpeng/Awesome-Face-Forgery-Generation-and-Detection
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To fill this gap, in this paper, we provide a comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of DeepFake detection, with more than 318 research papers carefully surveyed.
Multi-attentional Deepfake Detection
yoctta/multiple-attention
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CVPR 2021
Most of them model deepfake detection as a vanilla binary classification problem, i. e, first use a backbone network to extract a global feature and then feed it into a binary classifier (real/fake).
