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

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Showing 401–450 of 580 papers

TitleStatusHype
Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors—0
GazeForensics: DeepFake Detection via Gaze-guided Spatial Inconsistency Learning—0
AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Video Deepfake Detection—0
Deepfake detection by exploiting surface anomalies: the SurFake approach—0
Deepfake Detection: Leveraging the Power of 2D and 3D CNN Ensembles—0
AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting Multiple Experts for Video Deepfake Detection—0
Improving Video Deepfake Detection: A DCT-Based Approach with Patch-Level Analysis—0
Facial Forgery-based Deepfake Detection using Fine-Grained Features—0
Integrating Audio-Visual Features for Multimodal Deepfake Detection—0
MIS-AVoiDD: Modality Invariant and Specific Representation for Audio-Visual Deepfake Detection—0
How Close are Other Computer Vision Tasks to Deepfake Detection?—0
CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition—0
ProtoExplorer: Interpretable Forensic Analysis of Deepfake Videos using Prototype Exploration and Refinement—0
Characterizing the temporal dynamics of universal speech representations for generalizable deepfake detectionCode0
Quality-Agnostic Deepfake Detection with Intra-model Collaborative Learning—0
DF-TransFusion: Multimodal Deepfake Detection via Lip-Audio Cross-Attention and Facial Self-Attention—0
Towards generalisable and calibrated synthetic speech detection with self-supervised representations—0
Comparative Analysis of Deep-Fake Algorithms—0
Turn Fake into Real: Adversarial Head Turn Attacks Against Deepfake DetectionCode0
Real-time Detection of AI-Generated Speech for DeepFake Voice Conversion—0
Complex-valued neural networks for voice anti-spoofing—0
The DKU-DUKEECE System for the Manipulation Region Location Task of ADD 2023—0
Recap: Detecting Deepfake Video with Unpredictable Tampered Traces via Recovering Faces and Mapping Recovered Faces—0
Recent Advancements In The Field Of Deepfake Detection—0
How Generalizable are Deepfake Image Detectors? An Empirical StudyCode0
Deepfake Detection: A Comparative Analysis—0
Towards General Visual-Linguistic Face Forgery Detection—0
Self-Supervised Graph Transformer for Deepfake Detection—0
Towards Generalizable Deepfake Detection by Primary Region Regularization—0
MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential Deepfake Detection—0
Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality—0
TranssionADD: A multi-frame reinforcement based sequence tagging model for audio deepfake detection—0
Searching for the Fakes: Efficient Neural Architecture Search for General Face Forgery Detection—0
NPVForensics: Jointing Non-critical Phonemes and Visemes for Deepfake Detection—0
UNTAG: LEARNING GENERIC FEATURES FOR UNSUPERVISED TYPE-AGNOSTIC DEEPFAKE DETECTIONCode0
Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo ConversionCode0
Introducing Explicit Gaze Constraints to Face Swapping—0
Fooling State-of-the-Art Deepfake Detection with High-Quality Deepfakes—0
Can deepfakes be created by novice users?—0
Evading DeepFake Detectors via Adversarial Statistical Consistency—0
Investigation of ensemble methods for the detection of deepfake face manipulations—0
Assessment Framework for Deepfake Detection in Real-world Situations—0
Deepfake Detection of Occluded Images Using a Patch-based Approach—0
Deepfake Detection with Deep Learning: Convolutional Neural Networks versus Transformers—0
Leveraging Deep Learning Approaches for Deepfake Detection: A Review—0
LatentForensics: Towards frugal deepfake detection in the StyleGAN latent space—0
Diff-ID: An Explainable Identity Difference Quantification Framework for DeepFake Detection—0
Impact of Video Processing Operations in Deepfake Detection—0
Real Face Foundation Representation Learning for Generalized Deepfake Detection—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