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 326–350 of 580 papers

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