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 351–400 of 580 papers

TitleStatusHype
Deepfake Media Forensics: State of the Art and Challenges Ahead—0
DeepFake-o-meter: An Open Platform for DeepFake Detection—0
Deepfakes Detection with Automatic Face Weighting—0
Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward—0
DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning—0
Deepfake Video Forensics based on Transfer Learning—0
DeePhy: On Deepfake Phylogeny—0
Deep Learning for Deepfakes Creation and Detection: A Survey—0
Deep Learning Technology for Face Forgery Detection: A Survey—0
DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms—0
DeFakePro: Decentralized DeepFake Attacks Detection using ENF Authentication—0
Delocate: Detection and Localization for Deepfake Videos with Randomly-Located Tampered Traces—0
Delving into Sequential Patches for Deepfake Detection—0
Delving into the Frequency: Temporally Consistent Human Motion Transfer in the Fourier Space—0
DepthFake: a depth-based strategy for detecting Deepfake videos—0
Detecting Audio-Visual Deepfakes with Fine-Grained Inconsistencies—0
Detecting Deepfake by Creating Spatio-Temporal Regularity Disruption—0
Detecting Deepfake Videos: An Analysis of Three Techniques—0
Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality—0
DFCon: Attention-Driven Supervised Contrastive Learning for Robust Deepfake Detection—0
DF-Platter: Multi-Face Heterogeneous Deepfake Dataset—0
DF-TransFusion: Multimodal Deepfake Detection via Lip-Audio Cross-Attention and Facial Self-Attention—0
Diff-ID: An Explainable Identity Difference Quantification Framework for DeepFake Detection—0
Diffusion Deepfake—0
DIP: Diffusion Learning of Inconsistency Pattern for General DeepFake Detection—0
OGAN: Disrupting Deepfakes with an Adversarial Attack that Survives Training—0
Do Deepfake Detectors Work in Reality?—0
Does Audio Deepfake Detection Generalize?—0
DomainForensics: Exposing Face Forgery across Domains via Bi-directional Adaptation—0
DPL: Cross-quality DeepFake Detection via Dual Progressive Learning—0
Dynamic Graph Learning With Content-Guided Spatial-Frequency Relation Reasoning for Deepfake Detection—0
Easy, Interpretable, Effective: openSMILE for voice deepfake detection—0
ED^4: Explicit Data-level Debiasing for Deepfake Detection—0
EEG-Features for Generalized Deepfake Detection—0
Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors—0
Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues—0
Enhancing Deepfake Detection using SE Block Attention with CNN—0
EnvSDD: Benchmarking Environmental Sound Deepfake Detection—0
Evading DeepFake Detectors via Adversarial Statistical Consistency—0
Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectors—0
Exploiting Style Latent Flows for Generalizing Deepfake Video Detection—0
Exploring Active Data Selection Strategies for Continuous Training in Deepfake Detection—0
Exploring Adversarial Fake Images on Face Manifold—0
Exploring Strengths and Weaknesses of Super-Resolution Attack in Deepfake Detection—0
Exploring the Impact of Moire Pattern on Deepfake Detectors—0
Exploring Unbiased Deepfake Detection via Token-Level Shuffling and Mixing—0
Exploring WavLM Back-ends for Speech Spoofing and Deepfake Detection—0
Exposing Deepfake Face Forgeries with Guided Residuals—0
FaceGuard: Proactive Deepfake Detection—0
Face-LLaVA: Facial Expression and Attribute Understanding through Instruction Tuning—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