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

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Showing 176–200 of 580 papers

TitleStatusHype
Cross-Domain Audio Deepfake Detection: Dataset and Analysis—0
Audio Deepfake Detection with Self-Supervised WavLM and Multi-Fusion Attentive Classifier—0
Investigating the Impact of Pre-processing and Prediction Aggregation on the DeepFake Detection Task—0
Cross-Branch Orthogonality for Improved Generalization in Face Deepfake Detection—0
Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features—0
Cost Sensitive Optimization of Deepfake Detector—0
Contrastive Self-Supervised Learning of Global-Local Audio-Visual Representations—0
Attacker Attribution of Audio Deepfakes—0
Limits of Deepfake Detection: A Robust Estimation Viewpoint—0
Contrastive Learning of Global and Local Video Representations—0
Contrastive Learning for DeepFake Classification and Localization via Multi-Label Ranking—0
A Timely Survey on Vision Transformer for Deepfake Detection—0
Continuous fake media detection: adapting deepfake detectors to new generative techniques—0
Assessment Framework for Deepfake Detection in Real-world Situations—0
Context-aware TFL: A Universal Context-aware Contrastive Learning Framework for Temporal Forgery Localization—0
ASASVIcomtech: The Vicomtech-UGR Speech Deepfake Detection and SASV Systems for the ASVspoof5 Challenge—0
Adversarial Threats to DeepFake Detection: A Practical Perspective—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Complex-valued neural networks for voice anti-spoofing—0
ArVoice: A Multi-Speaker Dataset for Arabic Speech Synthesis—0
AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge—0
Comparative Analysis of Deepfake Detection Models: New Approaches and Perspectives—0
Comparative Analysis of Deep-Fake Algorithms—0
Adversarially robust deepfake media detection using fused convolutional neural network predictions—0
Multiple Contexts and Frequencies Aggregation Network forDeepfake Detection—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