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 551–580 of 580 papers

TitleStatusHype
Joint Audio-Visual Deepfake Detection—0
Identifying Invariant Texture Violation for Robust Deepfake Detection—0
Cost Sensitive Optimization of Deepfake Detector—0
Identity-Driven DeepFake Detection—0
Adversarial Threats to DeepFake Detection: A Practical Perspective—0
Spatio-temporal Features for Generalized Detection of Deepfake Videos—0
Dodging DeepFake Detection via Implicit Spatial-Domain Notch Filtering—0
Deepfake detection: humans vs. machines—0
DeepFake Detection Based on the Discrepancy Between the Face and its Context—0
On Attribution of Deepfakes—0
Two-branch Recurrent Network for Isolating Deepfakes in Videos—0
Fighting Deepfake by Exposing the Convolutional Traces on Images—0
Detecting Deepfake Videos: An Analysis of Three Techniques—0
Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes—0
OGAN: Disrupting Deepfakes with an Adversarial Attack that Survives Training—0
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow ReconstructionCode0
DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms—0
Investigating the Impact of Pre-processing and Prediction Aggregation on the DeepFake Detection Task—0
A Note on Deepfake Detection with Low-Resources—0
Advancing High Fidelity Identity Swapping for Forgery Detection—0
Deepfake Video Forensics based on Transfer Learning—0
Deepfakes Detection with Automatic Face Weighting—0
DeepFake Detection by Analyzing Convolutional Traces—0
Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues—0
DeepFake Detection: Current Challenges and Next Steps—0
Face X-ray for More General Face Forgery DetectionCode0
The Deepfake Detection Challenge (DFDC) Preview Dataset—0
Deep Learning for Deepfakes Creation and Detection: A Survey—0
Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder—0
Limits of Deepfake Detection: A Robust Estimation Viewpoint—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