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 276300 of 580 papers

TitleStatusHype
A Novel Framework for Assessment of Learning-based Detectors in Realistic Conditions with Application to Deepfake Detection0
A Preliminary Exploration with GPT-4o Voice Mode0
A Quality-Centric Framework for Generic Deepfake Detection0
Are Music Foundation Models Better at Singing Voice Deepfake Detection? Far-Better Fuse them with Speech Foundation Models0
A Review of Deep Learning-based Approaches for Deepfake Content Detection0
ArVoice: A Multi-Speaker Dataset for Arabic Speech Synthesis0
ASASVIcomtech: The Vicomtech-UGR Speech Deepfake Detection and SASV Systems for the ASVspoof5 Challenge0
Assessment Framework for Deepfake Detection in Real-world Situations0
A Timely Survey on Vision Transformer for Deepfake Detection0
Attacker Attribution of Audio Deepfakes0
Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features0
Audio Deepfake Detection with Self-Supervised WavLM and Multi-Fusion Attentive Classifier0
Audios Don't Lie: Multi-Frequency Channel Attention Mechanism for Audio Deepfake Detection0
Audio-Visual Deepfake Detection With Local Temporal Inconsistencies0
AUNet: Learning Relations Between Action Units for Face Forgery Detection0
AuthGuard: Generalizable Deepfake Detection via Language Guidance0
Automated Deepfake Detection0
AVFF: Audio-Visual Feature Fusion for Video Deepfake Detection0
AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Video Deepfake Detection0
AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting Multiple Experts for Video Deepfake Detection0
Benchmarking Audio Deepfake Detection Robustness in Real-world Communication Scenarios0
Benchmarking Deepart Detection0
Benchmarking Foundation Models for Zero-Shot Biometric Tasks0
Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues0
Beyond Face Swapping: A Diffusion-Based Digital Human Benchmark for Multimodal Deepfake Detection0
Show:102550
← PrevPage 12 of 24Next →

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