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 326350 of 580 papers

TitleStatusHype
Deepfake Caricatures: Amplifying attention to artifacts increases deepfake detection by humans and machines0
Deepfake Detection: A Comparative Analysis0
Deepfake Detection: A Comprehensive Survey from the Reliability Perspective0
Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM0
DeepFake Detection Based on the Discrepancy Between the Face and its Context0
DeepFake Detection by Analyzing Convolutional Traces0
Deepfake detection by exploiting surface anomalies: the SurFake approach0
DeepFake Detection: Current Challenges and Next Steps0
Deepfake Detection for Facial Images with Facemasks0
Deepfake detection: humans vs. machines0
Data-Driven Fairness Generalization for Deepfake Detection0
Deepfake detection in videos with multiple faces using geometric-fakeness features0
Deepfake Detection: Leveraging the Power of 2D and 3D CNN Ensembles0
Deepfake Detection of Face Images based on a Convolutional Neural Network0
Deepfake Detection of Occluded Images Using a Patch-based Approach0
Deepfake Detection of Singing Voices With Whisper Encodings0
Deepfake Detection System for the ADD Challenge Track 3.2 Based on Score Fusion0
Deepfake Detection using Biological Features: A Survey0
Deepfake Detection using ImageNet models and Temporal Images of 468 Facial Landmarks0
Deepfake Detection via Joint Unsupervised Reconstruction and Supervised Classification0
Deepfake Detection via Knowledge Injection0
Deepfake Detection with Deep Learning: Convolutional Neural Networks versus Transformers0
DeepFake Detection with Inconsistent Head Poses: Reproducibility and Analysis0
Deepfake Detection with Optimized Hybrid Model: EAR Biometric Descriptor via Improved RCNN0
DeepFake Doctor: Diagnosing and Treating Audio-Video Fake 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