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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 476–500 of 580 papers

TitleStatusHype
DeePhy: On Deepfake Phylogeny—0
Multimodal Graph Learning for Deepfake Detection—0
Deep Convolutional Pooling Transformer for Deepfake DetectionCode0
Protecting World Leader Using Facial Speaking Pattern Against Deepfakes—0
Delving into the Frequency: Temporally Consistent Human Motion Transfer in the Fourier Space—0
DepthFake: a depth-based strategy for detecting Deepfake videos—0
Deepfake Detection using ImageNet models and Temporal Images of 468 Facial Landmarks—0
Hybrid Transformer Network for Deepfake Detection—0
Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues—0
Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features—0
A Hybrid CNN-LSTM model for Video Deepfake Detection by Leveraging Optical Flow Features—0
Using Deep Learning to Detecting Deepfakes—0
DeFakePro: Decentralized DeepFake Attacks Detection using ENF Authentication—0
Detecting Deepfake by Creating Spatio-Temporal Regularity Disruption—0
Deepfake Face Traceability with Disentangling Reversing Network—0
Delving into Sequential Patches for Deepfake Detection—0
Identifying the Context Shift between Test Benchmarks and Production Data—0
Practical Deepfake Detection: Vulnerabilities in Global Contexts—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Deepfake Caricatures: Amplifying attention to artifacts increases deepfake detection by humans and machines—0
Real-centric Consistency Learning for Deepfake Detection—0
The Effectiveness of Temporal Dependency in Deepfake Video Detection—0
Exposing Deepfake Face Forgeries with Guided Residuals—0
The MeVer DeepFake Detection Service: Lessons Learnt from Developing and Deploying in the Wild—0
Metamorphic Testing-based Adversarial Attack to Fool Deepfake Detectors—0
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All datasetsFakeAVCelebFaceForensicsDFDC1CIFAKE: Real and AI-Generated Synthetic ImagesCOCOFakeDFFDLAV-DF

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AV-Lip-Sync+Accuracy (%)99.29—Unverified
2AvtenetAccuracy (%)98.57—Unverified
3FACTORROC AUC97.4—Unverified
4RealForensicsROC AUC97.1—Unverified
5AVADROC AUC94.5—Unverified
6AV-Lip-Sync ModelAccuracy (%)94—Unverified
7FTCNROC AUC93.1—Unverified
8LipForensicsROC AUC91.1—Unverified
9Multimodal Ensemble ModelAccuracy (%)89—Unverified
10AD DFDROC AUC88.1—Unverified
#ModelMetricClaimedVerifiedStatus
1XceptionNetDF96.36—Unverified
2QAD-EAUC0.96—Unverified
3EfficientNetB4 + EfficientNetB4ST + B4Att + B4AttSTAUC0.94—Unverified
4MARLIN (ViT-L)AUC0.94—Unverified
5MARLIN (ViT-B)AUC0.93—Unverified
6MARLIN (ViT-S)AUC0.89—Unverified
7EfficientNetB4 + EfficientNetB4ST + B4AttSTLogLoss0.33—Unverified
#ModelMetricClaimedVerifiedStatus
1Cross Efficient Vision TransformerAUC0.95—Unverified
2Efficient Vision TransformerAUC0.92—Unverified
3EfficientNetB4 + EfficientNetB4ST + B4AttLogLoss0.46—Unverified
#ModelMetricClaimedVerifiedStatus
1STYLE0L99—Unverified
#ModelMetricClaimedVerifiedStatus
1FasterThanLiesAUC99.65—Unverified
#ModelMetricClaimedVerifiedStatus
1FasterThanLiesAUC1—Unverified
#ModelMetricClaimedVerifiedStatus
1FasterThanLiesAUC1—Unverified
#ModelMetricClaimedVerifiedStatus
1BA-TFDAUC0.99—Unverified