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 301–350 of 580 papers

TitleStatusHype
CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation—0
Can deepfakes be created by novice users?—0
Can Multi-modal (reasoning) LLMs work as deepfake detectors?—0
Capture Artifacts via Progressive Disentangling and Purifying Blended Identities for Deepfake Detection—0
Classifying Deepfakes Using Swin Transformers—0
Codecfake: An Initial Dataset for Detecting LLM-based Deepfake Audio—0
Comparative Analysis of Deep Convolutional Neural Networks for Detecting Medical Image Deepfakes—0
Comparative Analysis of Deep-Fake Algorithms—0
Comparative Analysis of Deepfake Detection Models: New Approaches and Perspectives—0
Complex-valued neural networks for voice anti-spoofing—0
Context-aware TFL: A Universal Context-aware Contrastive Learning Framework for Temporal Forgery Localization—0
Continuous fake media detection: adapting deepfake detectors to new generative techniques—0
Contrastive Learning for DeepFake Classification and Localization via Multi-Label Ranking—0
Contrastive Learning of Global and Local Video Representations—0
Contrastive Self-Supervised Learning of Global-Local Audio-Visual Representations—0
Cost Sensitive Optimization of Deepfake Detector—0
Cross-Branch Orthogonality for Improved Generalization in Face Deepfake Detection—0
Cross-Domain Audio Deepfake Detection: Dataset and Analysis—0
Cross-Domain Local Characteristic Enhanced Deepfake Video Detection—0
Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review—0
DDL: A Dataset for Interpretable Deepfake Detection and Localization in Real-World Scenarios—0
DeePen: Penetration Testing for Audio Deepfake Detection—0
Deepfake Audio Detection Using Spectrogram-based Feature and Ensemble of Deep Learning Models—0
Deepfake Caricatures: Amplifying attention to artifacts increases deepfake detection by humans and machines—0
Deepfake Detection: A Comparative Analysis—0
Deepfake Detection: A Comprehensive Survey from the Reliability Perspective—0
Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM—0
DeepFake Detection Based on the Discrepancy Between the Face and its Context—0
DeepFake Detection by Analyzing Convolutional Traces—0
Deepfake detection by exploiting surface anomalies: the SurFake approach—0
DeepFake Detection: Current Challenges and Next Steps—0
Deepfake Detection for Facial Images with Facemasks—0
Deepfake detection: humans vs. machines—0
Data-Driven Fairness Generalization for Deepfake Detection—0
Deepfake detection in videos with multiple faces using geometric-fakeness features—0
Deepfake Detection: Leveraging the Power of 2D and 3D CNN Ensembles—0
Deepfake Detection of Face Images based on a Convolutional Neural Network—0
Deepfake Detection of Occluded Images Using a Patch-based Approach—0
Deepfake Detection of Singing Voices With Whisper Encodings—0
Deepfake Detection System for the ADD Challenge Track 3.2 Based on Score Fusion—0
Deepfake Detection using Biological Features: A Survey—0
Deepfake Detection using ImageNet models and Temporal Images of 468 Facial Landmarks—0
Deepfake Detection via Joint Unsupervised Reconstruction and Supervised Classification—0
Deepfake Detection via Knowledge Injection—0
Deepfake Detection with Deep Learning: Convolutional Neural Networks versus Transformers—0
DeepFake Detection with Inconsistent Head Poses: Reproducibility and Analysis—0
Deepfake Detection with Optimized Hybrid Model: EAR Biometric Descriptor via Improved RCNN—0
DeepFake Doctor: Diagnosing and Treating Audio-Video Fake Detection—0
Deepfake Face Traceability with Disentangling Reversing Network—0
Mover: Mask and Recovery based Facial Part Consistency Aware Method for Deepfake Video Detection—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