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 301–350 of 580 papers

TitleStatusHype
Unsupervised Multimodal Deepfake Detection Using Intra- and Cross-Modal Inconsistencies—0
AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake DatasetCode1
Unearthing Common Inconsistency for Generalisable Deepfake Detection—0
Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake DetectionCode3
Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors—0
GazeForensics: DeepFake Detection via Gaze-guided Spatial Inconsistency Learning—0
Weakly-supervised deepfake localization in diffusion-generated imagesCode1
AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Video Deepfake Detection—0
Detecting Deepfakes Without Seeing AnyCode1
Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked AutoencodersCode1
Deepfake detection by exploiting surface anomalies: the SurFake approach—0
AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image DetectorsCode1
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
Robust Sequential DeepFake DetectionCode1
Detecting and Grounding Multi-Modal Media Manipulation and BeyondCode2
Contrastive Pseudo Learning for Open-World DeepFake AttributionCode1
Locate and Verify: A Two-Stream Network for Improved Deepfake DetectionCode1
ProtoExplorer: Interpretable Forensic Analysis of Deepfake Videos using Prototype Exploration and Refinement—0
DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery CluesCode1
Characterizing the temporal dynamics of universal speech representations for generalizable deepfake detectionCode0
HM-Conformer: A Conformer-based audio deepfake detection system with hierarchical pooling and multi-level classification token aggregation methodsCode1
SingFake: Singing Voice Deepfake DetectionCode1
DF-TransFusion: Multimodal Deepfake Detection via Lip-Audio Cross-Attention and Facial Self-Attention—0
Quality-Agnostic Deepfake Detection with Intra-model Collaborative Learning—0
Towards generalisable and calibrated synthetic speech detection with self-supervised representations—0
Comparative Analysis of Deep-Fake Algorithms—0
FSD: An Initial Chinese Dataset for Fake Song DetectionCode1
Robustness and Generalizability of Deepfake Detection: A Study with Diffusion ModelsCode1
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
OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated ExamplesCode1
Deepfake Video Detection Using Generative Convolutional Vision TransformerCode1
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
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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