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 551580 of 580 papers

TitleStatusHype
TweepFake: about Detecting Deepfake TweetsCode1
Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training DataCode1
Detecting Deepfake Videos: An Analysis of Three Techniques0
Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes0
Deepfake Detection using Spatiotemporal Convolutional NetworksCode1
OGAN: Disrupting Deepfakes with an Adversarial Attack that Survives Training0
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow ReconstructionCode0
DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms0
The DeepFake Detection Challenge (DFDC) DatasetCode1
Investigating the Impact of Pre-processing and Prediction Aggregation on the DeepFake Detection Task0
A Note on Deepfake Detection with Low-Resources0
Advancing High Fidelity Identity Swapping for Forgery Detection0
Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and LocalizationCode1
Deepfake Video Forensics based on Transfer Learning0
Deepfakes Detection with Automatic Face Weighting0
DeepFake Detection by Analyzing Convolutional Traces0
Video Face Manipulation Detection Through Ensemble of CNNsCode1
Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues0
DeepFake Detection: Current Challenges and Next Steps0
Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial ExamplesCode1
DeepFakes and Beyond: A Survey of Face Manipulation and Fake DetectionCode4
Face X-ray for More General Face Forgery DetectionCode0
Unmasking DeepFakes with simple FeaturesCode1
The Deepfake Detection Challenge (DFDC) Preview Dataset0
Celeb-DF: A Large-scale Challenging Dataset for DeepFake ForensicsCode1
Deep Learning for Deepfakes Creation and Detection: A Survey0
Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder0
Limits of Deepfake Detection: A Robust Estimation Viewpoint0
FaceForensics++: Learning to Detect Manipulated Facial ImagesCode1
MesoNet: a Compact Facial Video Forgery Detection NetworkCode1
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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