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 401–450 of 580 papers

TitleStatusHype
A Dataless FaceSwap Detection Approach Using Synthetic ImagesCode0
FakeOut: Leveraging Out-of-domain Self-supervision for Multi-modal Video Deepfake DetectionCode1
Deepfake Detection via Joint Unsupervised Reconstruction and Supervised Classification—0
Deepfake Detection: A Comprehensive Survey from the Reliability Perspective—0
MINTIME: Multi-Identity Size-Invariant Video Deepfake DetectionCode1
Generalizable Deepfake Detection with Phase-Based Motion Analysis—0
MARLIN: Masked Autoencoder for facial video Representation LearnINgCode2
Lip Sync Matters: A Novel Multimodal Forgery DetectorCode0
Cross-Domain Local Characteristic Enhanced Deepfake Video Detection—0
Exploring Spatial-Temporal Features for Deepfake Detection and LocalizationCode1
Anisotropic multiresolution analyses for deepfake detection—0
Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection GeneralizationCode1
Deepfake Detection System for the ADD Challenge Track 3.2 Based on Score Fusion—0
GOTCHA: Real-Time Video Deepfake Detection via Challenge-ResponseCode1
SpecRNet: Towards Faster and More Accessible Audio DeepFake DetectionCode1
Aggregating Layers for Deepfake DetectionCode0
A Machine Learning Approach for DeepFake Detection—0
When Handcrafted Features and Deep Features Meet Mismatched Training and Test Sets for Deepfake Detection—0
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
Analyzing Fairness in Deepfake Detection With Massively Annotated DatabasesCode1
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
GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake DetectionCode1
Detecting Deepfake by Creating Spatio-Temporal Regularity Disruption—0
Explaining Deepfake Detection by Analysing Image MatchingCode1
Deepfake Face Traceability with Disentangling Reversing Network—0
Delving into Sequential Patches for Deepfake Detection—0
Detecting and Recovering Sequential DeepFake ManipulationCode1
Identifying the Context Shift between Test Benchmarks and Production Data—0
Cross-Forgery Analysis of Vision Transformers and CNNs for Deepfake Image DetectionCode1
Attack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake DetectionCode1
Practical Deepfake Detection: Vulnerabilities in Global Contexts—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Is Synthetic Voice Detection Research Going Into the Right Direction?Code1
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
A Continual Deepfake Detection Benchmark: Dataset, Methods, and EssentialsCode1
Exposing Deepfake Face Forgeries with Guided Residuals—0
The MeVer DeepFake Detection Service: Lessons Learnt from Developing and Deploying in the Wild—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