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 401425 of 580 papers

TitleStatusHype
Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors0
GazeForensics: DeepFake Detection via Gaze-guided Spatial Inconsistency Learning0
AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Video Deepfake Detection0
Deepfake detection by exploiting surface anomalies: the SurFake approach0
Deepfake Detection: Leveraging the Power of 2D and 3D CNN Ensembles0
AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting Multiple Experts for Video Deepfake Detection0
Improving Video Deepfake Detection: A DCT-Based Approach with Patch-Level Analysis0
Facial Forgery-based Deepfake Detection using Fine-Grained Features0
Integrating Audio-Visual Features for Multimodal Deepfake Detection0
MIS-AVoiDD: Modality Invariant and Specific Representation for Audio-Visual Deepfake Detection0
How Close are Other Computer Vision Tasks to Deepfake Detection?0
CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition0
ProtoExplorer: Interpretable Forensic Analysis of Deepfake Videos using Prototype Exploration and Refinement0
Characterizing the temporal dynamics of universal speech representations for generalizable deepfake detectionCode0
Quality-Agnostic Deepfake Detection with Intra-model Collaborative Learning0
DF-TransFusion: Multimodal Deepfake Detection via Lip-Audio Cross-Attention and Facial Self-Attention0
Towards generalisable and calibrated synthetic speech detection with self-supervised representations0
Comparative Analysis of Deep-Fake Algorithms0
Turn Fake into Real: Adversarial Head Turn Attacks Against Deepfake DetectionCode0
Real-time Detection of AI-Generated Speech for DeepFake Voice Conversion0
Complex-valued neural networks for voice anti-spoofing0
The DKU-DUKEECE System for the Manipulation Region Location Task of ADD 20230
Recap: Detecting Deepfake Video with Unpredictable Tampered Traces via Recovering Faces and Mapping Recovered Faces0
Recent Advancements In The Field Of Deepfake Detection0
How Generalizable are Deepfake Image Detectors? An Empirical StudyCode0
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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