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 5175 of 580 papers

TitleStatusHype
Defense Against Adversarial Attacks on Audio DeepFake DetectionCode1
AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image DetectorsCode1
Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training DataCode1
Deepfake Video Detection Using Generative Convolutional Vision TransformerCode1
DefakeHop: A Light-Weight High-Performance Deepfake DetectorCode1
Detecting Deepfakes Without Seeing AnyCode1
Deepfake Network Architecture AttributionCode1
Attack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake DetectionCode1
DeepFake MNIST+: A DeepFake Facial Animation DatasetCode1
DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery CluesCode1
DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized Deepfake DetectionCode1
Deepfake Forensics via An Adversarial GameCode1
Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024Code1
Deepfake Media Generation and Detection in the Generative AI Era: A Survey and OutlookCode1
DeepFake-Adapter: Dual-Level Adapter for DeepFake DetectionCode1
Deepfake Detection Scheme Based on Vision Transformer and DistillationCode1
Data-Independent Operator: A Training-Free Artifact Representation Extractor for Generalizable Deepfake DetectionCode1
Contextual Cross-Modal Attention for Audio-Visual Deepfake Detection and LocalizationCode1
DeCLIP: Decoding CLIP representations for deepfake localizationCode1
Deepfake Detection using Spatiotemporal Convolutional NetworksCode1
DeepFakesON-Phys: DeepFakes Detection based on Heart Rate EstimationCode1
Cross-Forgery Analysis of Vision Transformers and CNNs for Deepfake Image DetectionCode1
Countering Malicious DeepFakes: Survey, Battleground, and HorizonCode1
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake DetectionCode1
AVT2-DWF: Improving Deepfake Detection with Audio-Visual Fusion and Dynamic Weighting StrategiesCode1
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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