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 351375 of 580 papers

TitleStatusHype
Deepfake Face Traceability with Disentangling Reversing Network0
Mover: Mask and Recovery based Facial Part Consistency Aware Method for Deepfake Video Detection0
Deepfake Media Forensics: State of the Art and Challenges Ahead0
DeepFake-o-meter: An Open Platform for DeepFake Detection0
Deepfakes Detection with Automatic Face Weighting0
Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward0
DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning0
Deepfake Video Forensics based on Transfer Learning0
DeePhy: On Deepfake Phylogeny0
Deep Learning for Deepfakes Creation and Detection: A Survey0
Deep Learning Technology for Face Forgery Detection: A Survey0
DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms0
DeFakePro: Decentralized DeepFake Attacks Detection using ENF Authentication0
Delocate: Detection and Localization for Deepfake Videos with Randomly-Located Tampered Traces0
Delving into Sequential Patches for Deepfake Detection0
Delving into the Frequency: Temporally Consistent Human Motion Transfer in the Fourier Space0
DepthFake: a depth-based strategy for detecting Deepfake videos0
Detecting Audio-Visual Deepfakes with Fine-Grained Inconsistencies0
Detecting Deepfake by Creating Spatio-Temporal Regularity Disruption0
Detecting Deepfake Videos: An Analysis of Three Techniques0
Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality0
DFCon: Attention-Driven Supervised Contrastive Learning for Robust Deepfake Detection0
DF-Platter: Multi-Face Heterogeneous Deepfake Dataset0
DF-TransFusion: Multimodal Deepfake Detection via Lip-Audio Cross-Attention and Facial Self-Attention0
Diff-ID: An Explainable Identity Difference Quantification Framework for DeepFake Detection0
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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