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 251–275 of 580 papers

TitleStatusHype
Evading DeepFake Detectors via Adversarial Statistical Consistency—0
EnvSDD: Benchmarking Environmental Sound Deepfake Detection—0
Cost Sensitive Optimization of Deepfake Detector—0
Enhancing Deepfake Detection using SE Block Attention with CNN—0
Contrastive Self-Supervised Learning of Global-Local Audio-Visual Representations—0
Attacker Attribution of Audio Deepfakes—0
Investigating the Impact of Pre-processing and Prediction Aggregation on the DeepFake Detection Task—0
Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues—0
Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors—0
EEG-Features for Generalized Deepfake Detection—0
Contrastive Learning of Global and Local Video Representations—0
ED^4: Explicit Data-level Debiasing for Deepfake Detection—0
Easy, Interpretable, Effective: openSMILE for voice deepfake detection—0
Dynamic Graph Learning With Content-Guided Spatial-Frequency Relation Reasoning for Deepfake Detection—0
DPL: Cross-quality DeepFake Detection via Dual Progressive Learning—0
Contrastive Learning for DeepFake Classification and Localization via Multi-Label Ranking—0
A Timely Survey on Vision Transformer for Deepfake Detection—0
DomainForensics: Exposing Face Forgery across Domains via Bi-directional Adaptation—0
Does Audio Deepfake Detection Generalize?—0
Continuous fake media detection: adapting deepfake detectors to new generative techniques—0
Do Deepfake Detectors Work in Reality?—0
OGAN: Disrupting Deepfakes with an Adversarial Attack that Survives Training—0
DIP: Diffusion Learning of Inconsistency Pattern for General DeepFake Detection—0
Context-aware TFL: A Universal Context-aware Contrastive Learning Framework for Temporal Forgery Localization—0
Assessment Framework for Deepfake Detection in Real-world Situations—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