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 526550 of 580 papers

TitleStatusHype
Finding Facial Forgery Artifacts with Parts-Based Detectors0
MD-CSDNetwork: Multi-Domain Cross Stitched Network for Deepfake Detection0
FaceGuard: Proactive Deepfake Detection0
Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectors0
DeepFake Detection with Inconsistent Head Poses: Reproducibility and Analysis0
BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection0
Video Transformer for Deepfake Detection with Incremental Learning0
Human Perception of Audio Deepfakes0
Understanding the Security of Deepfake Detection0
Automated Deepfake Detection0
FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning0
Deepfake Detection by Human Crowds, Machines, and Machine-informed CrowdsCode0
TAR: Generalized Forensic Framework to Detect Deepfakes using Weakly Supervised LearningCode0
What's wrong with this video? Comparing Explainers for Deepfake Detection0
An Examination of Fairness of AI Models for Deepfake Detection0
DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning0
Towards Measuring Fairness in AI: the Casual Conversations Dataset0
KoDF: A Large-scale Korean DeepFake Detection Dataset0
DeepFake-o-meter: An Open Platform for DeepFake Detection0
Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward0
The Deepfake Detection Dilemma: A Multistakeholder Exploration of Adversarial Dynamics in Synthetic Media0
Adversarially robust deepfake media detection using fused convolutional neural network predictions0
Fighting deepfakes by detecting GAN DCT anomalies0
Exploring Adversarial Fake Images on Face Manifold0
Contrastive Self-Supervised Learning of Global-Local Audio-Visual Representations0
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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