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 351375 of 580 papers

TitleStatusHype
Straight Through Gumbel Softmax Estimator based Bimodal Neural Architecture Search for Audio-Visual Deepfake Detection0
Decoupling Forgery Semantics for Generalizable Deepfake DetectionCode0
Real-Time Deepfake Detection in the Real-World0
Codecfake: An Initial Dataset for Detecting LLM-based Deepfake Audio0
Continuous fake media detection: adapting deepfake detectors to new generative techniques0
AVFF: Audio-Visual Feature Fusion for Video Deepfake Detection0
Harder or Different? Understanding Generalization of Audio Deepfake Detection0
Text Modality Oriented Image Feature Extraction for Detecting Diffusion-based DeepFake0
A3:Ambiguous Aberrations Captured via Astray-Learning for Facial Forgery Semantic Sublimation0
A Timely Survey on Vision Transformer for Deepfake Detection0
PolyGlotFake: A Novel Multilingual and Multimodal DeepFake DatasetCode0
EEG-Features for Generalized Deepfake Detection0
Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakes0
Training-Free Deepfake Voice Recognition by Leveraging Large-Scale Pre-Trained Models0
Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative AnalysisCode0
In Anticipation of Perfect Deepfake: Identity-anchored Artifact-agnostic Detection under Rebalanced Deepfake Detection Protocol0
Towards Quantitative Evaluation of Explainable AI Methods for Deepfake DetectionCode0
Are Watermarks Bugs for Deepfake Detectors? Rethinking Proactive ForensicsCode0
Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities0
Texture, Shape and Order Matter: A New Transformer Design for Sequential DeepFake Detection0
Retrieval-Augmented Audio Deepfake Detection0
FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledge0
Cross-Domain Audio Deepfake Detection: Dataset and Analysis0
Diffusion Deepfake0
Heterogeneity over Homogeneity: Investigating Multilingual Speech Pre-Trained Models for Detecting Audio DeepfakeCode0
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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