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 326350 of 580 papers

TitleStatusHype
UniForensics: Face Forgery Detection via General Facial Representation0
Unlocking the Capabilities of Vision-Language Models for Generalizable and Explainable Deepfake Detection0
Unmasking Deep Fakes: Leveraging Deep Learning for Video Authenticity Detection0
Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakes0
Unsupervised Multimodal Deepfake Detection Using Intra- and Cross-Modal Inconsistencies0
Using Deep Learning to Detecting Deepfakes0
USTC-KXDIGIT System Description for ASVspoof5 Challenge0
Video Transformer for Deepfake Detection with Incremental Learning0
Visual Watermarking in the Era of Diffusion Models: Advances and Challenges0
VLForgery Face Triad: Detection, Localization and Attribution via Multimodal Large Language Models0
What Does an Audio Deepfake Detector Focus on? A Study in the Time Domain0
What's wrong with this video? Comparing Explainers for Deepfake Detection0
When Handcrafted Features and Deep Features Meet Mismatched Training and Test Sets for Deepfake Detection0
Why Do Facial Deepfake Detectors Fail?0
The Deepfake Detection Dilemma: A Multistakeholder Exploration of Adversarial Dynamics in Synthetic Media0
0-1 laws for pattern occurrences in phylogenetic trees and networks0
Limits of Deepfake Detection: A Robust Estimation Viewpoint0
Multiple Contexts and Frequencies Aggregation Network forDeepfake Detection0
ALLM4ADD: Unlocking the Capabilities of Audio Large Language Models for Audio Deepfake Detection0
MAVOS-DD: Multilingual Audio-Video Open-Set Deepfake Detection Benchmark0
A3:Ambiguous Aberrations Captured via Astray-Learning for Facial Forgery Semantic Sublimation0
AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge0
A Data-Driven Diffusion-based Approach for Audio Deepfake Explanations0
ADD 2022: the First Audio Deep Synthesis Detection Challenge0
ADD 2023: Towards Audio Deepfake Detection and Analysis in the Wild0
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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