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 176200 of 580 papers

TitleStatusHype
Standing on the Shoulders of Giants: Reprogramming Visual-Language Model for General Deepfake Detection0
USTC-KXDIGIT System Description for ASVspoof5 Challenge0
Speech Foundation Model Ensembles for the Controlled Singing Voice Deepfake Detection (CtrSVDD) Challenge 2024Code2
AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge0
Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding ApproachCode0
SVDD 2024: The Inaugural Singing Voice Deepfake Detection ChallengeCode1
Easy, Interpretable, Effective: openSMILE for voice deepfake detection0
SONICS: Synthetic Or Not -- Identifying Counterfeit SongsCode1
2D-Malafide: Adversarial Attacks Against Face Deepfake Detection SystemsCode0
Analyzing the Impact of Splicing Artifacts in Partially Fake Speech Signals0
Guided and Fused: Efficient Frozen CLIP-ViT with Feature Guidance and Multi-Stage Feature Fusion for Generalizable Deepfake Detection0
Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style MixtureCode0
Does Current Deepfake Audio Detection Model Effectively Detect ALM-based Deepfake Audio?Code0
ASASVIcomtech: The Vicomtech-UGR Speech Deepfake Detection and SASV Systems for the ASVspoof5 Challenge0
C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake DetectionCode2
Penny-Wise and Pound-Foolish in Deepfake DetectionCode0
WavLM model ensemble for audio deepfake detectionCode0
IDRetracor: Towards Visual Forensics Against Malicious Face Swapping0
Temporal Variability and Multi-Viewed Self-Supervised Representations to Tackle the ASVspoof5 Deepfake Challenge0
Detecting Audio-Visual Deepfakes with Fine-Grained Inconsistencies0
ED^4: Explicit Data-level Debiasing for Deepfake Detection0
ADD 2023: Towards Audio Deepfake Detection and Analysis in the Wild0
WWW: Where, Which and Whatever Enhancing Interpretability in Multimodal Deepfake DetectionCode0
Multiple Contexts and Frequencies Aggregation Network forDeepfake Detection0
Contextual Cross-Modal Attention for Audio-Visual Deepfake Detection and LocalizationCode1
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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