SOTAVerified

DeepFake Detection

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

Papers

Showing 151175 of 580 papers

TitleStatusHype
DefakeHop: A Light-Weight High-Performance Deepfake DetectorCode1
Deepfake Videos in the Wild: Analysis and DetectionCode1
Multi-attentional Deepfake DetectionCode1
Countering Malicious DeepFakes: Survey, Battleground, and HorizonCode1
Deepfake Video Detection Using Convolutional Vision TransformerCode1
Towards Solving the DeepFake Problem : An Analysis on Improving DeepFake Detection using Dynamic Face AugmentationCode1
WildDeepfake: A Challenging Real-World Dataset for Deepfake DetectionCode1
Taming Transformers for High-Resolution Image SynthesisCode1
Learning Self-Consistency for Deepfake DetectionCode1
Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery DetectionCode1
Neural Deepfake Detection with Factual Structure of TextCode1
DeepFakesON-Phys: DeepFakes Detection based on Heart Rate EstimationCode1
A Convolutional LSTM based Residual Network for Deepfake Video DetectionCode1
TweepFake: about Detecting Deepfake TweetsCode1
Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training DataCode1
Deepfake Detection using Spatiotemporal Convolutional NetworksCode1
The DeepFake Detection Challenge (DFDC) DatasetCode1
Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and LocalizationCode1
Video Face Manipulation Detection Through Ensemble of CNNsCode1
Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial ExamplesCode1
Unmasking DeepFakes with simple FeaturesCode1
Celeb-DF: A Large-scale Challenging Dataset for DeepFake ForensicsCode1
FaceForensics++: Learning to Detect Manipulated Facial ImagesCode1
MesoNet: a Compact Facial Video Forgery Detection NetworkCode1
SHIELD: A Secure and Highly Enhanced Integrated Learning for Robust Deepfake Detection against Adversarial Attacks0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AV-Lip-Sync+Accuracy (%)99.29Unverified
2AvtenetAccuracy (%)98.57Unverified
3FACTORROC AUC97.4Unverified
4RealForensicsROC AUC97.1Unverified
5AVADROC AUC94.5Unverified
6AV-Lip-Sync ModelAccuracy (%)94Unverified
7FTCNROC AUC93.1Unverified
8LipForensicsROC AUC91.1Unverified
9Multimodal Ensemble ModelAccuracy (%)89Unverified
10AD DFDROC AUC88.1Unverified
#ModelMetricClaimedVerifiedStatus
1XceptionNetDF96.36Unverified
2QAD-EAUC0.96Unverified
3EfficientNetB4 + EfficientNetB4ST + B4Att + B4AttSTAUC0.94Unverified
4MARLIN (ViT-L)AUC0.94Unverified
5MARLIN (ViT-B)AUC0.93Unverified
6MARLIN (ViT-S)AUC0.89Unverified
7EfficientNetB4 + EfficientNetB4ST + B4AttSTLogLoss0.33Unverified
#ModelMetricClaimedVerifiedStatus
1Cross Efficient Vision TransformerAUC0.95Unverified
2Efficient Vision TransformerAUC0.92Unverified
3EfficientNetB4 + EfficientNetB4ST + B4AttLogLoss0.46Unverified
#ModelMetricClaimedVerifiedStatus
1STYLE0L99Unverified
#ModelMetricClaimedVerifiedStatus
1FasterThanLiesAUC99.65Unverified
#ModelMetricClaimedVerifiedStatus
1FasterThanLiesAUC1Unverified
#ModelMetricClaimedVerifiedStatus
1FasterThanLiesAUC1Unverified
#ModelMetricClaimedVerifiedStatus
1BA-TFDAUC0.99Unverified