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 401425 of 580 papers

TitleStatusHype
FaceGuard: Proactive Deepfake Detection0
Face-LLaVA: Facial Expression and Attribute Understanding through Instruction Tuning0
FaceShield: Defending Facial Image against Deepfake Threats0
Facial Features Matter: a Dynamic Watermark based Proactive Deepfake Detection Approach0
Facial Forgery-based Deepfake Detection using Fine-Grained Features0
Fairness Evaluation in Deepfake Detection Models using Metamorphic Testing0
Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities0
Fake It till You Make It: Curricular Dynamic Forgery Augmentations towards General Deepfake Detection0
Dodging DeepFake Detection via Implicit Spatial-Domain Notch Filtering0
FakeTransformer: Exposing Face Forgery From Spatial-Temporal Representation Modeled By Facial Pixel Variations0
FauForensics: Boosting Audio-Visual Deepfake Detection with Facial Action Units0
Fighting Deepfake by Exposing the Convolutional Traces on Images0
Fighting deepfakes by detecting GAN DCT anomalies0
Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection0
Finding Facial Forgery Artifacts with Parts-Based Detectors0
Fooling State-of-the-Art Deepfake Detection with High-Quality Deepfakes0
Forensic deepfake audio detection using segmental speech features0
FractalForensics: Proactive Deepfake Detection and Localization via Fractal Watermarks0
FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbations0
Self-Supervised Graph Transformer for Deepfake Detection0
Self-supervised Transformer for Deepfake Detection0
Shaking the Fake: Detecting Deepfake Videos in Real Time via Active Probes0
SHIELD: A Secure and Highly Enhanced Integrated Learning for Robust Deepfake Detection against Adversarial Attacks0
SLIM: Style-Linguistics Mismatch Model for Generalized Audio Deepfake Detection0
Social Media Authentication and Combating Deepfakes using Semi-fragile Invisible Image Watermarking0
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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