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 501–550 of 580 papers

TitleStatusHype
FFR_FD: Effective and Fast Detection of DeepFakes Based on Feature Point DefectsCode1
Understanding the Security of Deepfake Detection—0
Automated Deepfake Detection—0
Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated ImagesCode1
DFGC 2021: A DeepFake Game CompetitionCode1
FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning—0
Deepfake Detection by Human Crowds, Machines, and Machine-informed CrowdsCode0
TAR: Generalized Forensic Framework to Detect Deepfakes using Weakly Supervised LearningCode0
What's wrong with this video? Comparing Explainers for Deepfake Detection—0
One Shot Face Swapping on MegapixelsCode1
An Examination of Fairness of AI Models for Deepfake Detection—0
DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning—0
M2TR: Multi-modal Multi-scale Transformers for Deepfake DetectionCode1
Contrastive Learning of Global-Local Video RepresentationsCode1
Towards Measuring Fairness in AI: the Casual Conversations Dataset—0
Deepfake Detection Scheme Based on Vision Transformer and DistillationCode1
Deepfake Forensics via An Adversarial GameCode1
KoDF: A Large-scale Korean DeepFake Detection Dataset—0
DefakeHop: A Light-Weight High-Performance Deepfake DetectorCode1
Deepfake Videos in the Wild: Analysis and DetectionCode1
Multi-attentional Deepfake DetectionCode1
DeepFake-o-meter: An Open Platform for DeepFake Detection—0
Countering Malicious DeepFakes: Survey, Battleground, and HorizonCode1
Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward—0
Deepfake Video Detection Using Convolutional Vision TransformerCode1
Towards Solving the DeepFake Problem : An Analysis on Improving DeepFake Detection using Dynamic Face AugmentationCode1
Adversarially robust deepfake media detection using fused convolutional neural network predictions—0
The Deepfake Detection Dilemma: A Multistakeholder Exploration of Adversarial Dynamics in Synthetic Media—0
Fighting deepfakes by detecting GAN DCT anomalies—0
Exploring Adversarial Fake Images on Face Manifold—0
WildDeepfake: A Challenging Real-World Dataset for Deepfake DetectionCode1
Joint Audio-Visual Deepfake Detection—0
Contrastive Self-Supervised Learning of Global-Local Audio-Visual Representations—0
Identifying Invariant Texture Violation for Robust Deepfake Detection—0
Taming Transformers for High-Resolution Image SynthesisCode1
Learning Self-Consistency for Deepfake DetectionCode1
Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery DetectionCode1
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
Neural Deepfake Detection with Factual Structure of TextCode1
DeepFakesON-Phys: DeepFakes Detection based on Heart Rate EstimationCode1
Dodging DeepFake Detection via Implicit Spatial-Domain Notch Filtering—0
A Convolutional LSTM based Residual Network for Deepfake Video DetectionCode1
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
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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