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 2650 of 580 papers

TitleStatusHype
Detecting Deepfakes with Self-Blended ImagesCode2
Audio Deepfake Detection with Self-Supervised XLS-R and SLS ClassifierCode2
Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake DetectionCode2
MARLIN: Masked Autoencoder for facial video Representation LearnINgCode2
ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and LocalizationCode2
DeMamba: AI-Generated Video Detection on Million-Scale GenVideo BenchmarkCode2
Detecting music deepfakes is easy but actually hardCode2
Detecting and Grounding Multi-Modal Media ManipulationCode2
Detecting and Grounding Multi-Modal Media Manipulation and BeyondCode2
Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space LearningCode2
C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake DetectionCode2
DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable DiffusionCode2
DeepFake MNIST+: A DeepFake Facial Animation DatasetCode1
A Continual Deepfake Detection Benchmark: Dataset, Methods, and EssentialsCode1
Deepfake Network Architecture AttributionCode1
Analyzing Fairness in Deepfake Detection With Massively Annotated DatabasesCode1
Adversarial Magnification to Deceive Deepfake Detection through Super ResolutionCode1
Deepfake Media Generation and Detection in the Generative AI Era: A Survey and OutlookCode1
Deepfake Detection using Spatiotemporal Convolutional NetworksCode1
Deepfake Detection Scheme Based on Vision Transformer and DistillationCode1
Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024Code1
Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial ExamplesCode1
DeepFake-Adapter: Dual-Level Adapter for DeepFake DetectionCode1
AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image DetectorsCode1
Attack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake DetectionCode1
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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