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

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Showing 501–550 of 580 papers

TitleStatusHype
Does Audio Deepfake Detection Generalize?—0
Attacker Attribution of Audio Deepfakes—0
A Novel Framework for Assessment of Learning-based Detectors in Realistic Conditions with Application to Deepfake Detection—0
A New Approach to Improve Learning-based Deepfake Detection in Realistic Conditions—0
Transferable Class-Modelling for Decentralized Source Attribution of GAN-Generated ImagesCode0
Fairness Evaluation in Deepfake Detection Models using Metamorphic Testing—0
Towards Benchmarking and Evaluating Deepfake Detection—0
The Vicomtech Audio Deepfake Detection System based on Wav2Vec2 for the 2022 ADD Challenge—0
Self-supervised Transformer for Deepfake Detection—0
Deepfake Detection for Facial Images with Facemasks—0
Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era—0
ADD 2022: the First Audio Deep Synthesis Detection Challenge—0
Robust Deepfake On Unrestricted Media: Generation And Detection—0
A Review of Deep Learning-based Approaches for Deepfake Content Detection—0
D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles—0
FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbations—0
Block shuffling learning for Deepfake DetectionCode0
ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake ImagesCode0
Contrastive Learning of Global and Local Video Representations—0
A War Beyond Deepfake: Benchmarking Facial Counterfeits and Countermeasures—0
FakeTransformer: Exposing Face Forgery From Spatial-Temporal Representation Modeled By Facial Pixel Variations—0
Impact of Benign Modifications on Discriminative Performance of Deepfake Detectors—0
MC-LCR: Multi-modal contrastive classification by locally correlated representations for effective face forgery detection—0
An Experimental Evaluation on Deepfake Detection using Deep Face Recognition—0
Machine Learning based Medical Image Deepfake Detection: A Comparative Study—0
Finding Facial Forgery Artifacts with Parts-Based Detectors—0
MD-CSDNetwork: Multi-Domain Cross Stitched Network for Deepfake Detection—0
FaceGuard: Proactive Deepfake Detection—0
Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectors—0
DeepFake Detection with Inconsistent Head Poses: Reproducibility and Analysis—0
BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection—0
Video Transformer for Deepfake Detection with Incremental Learning—0
Human Perception of Audio Deepfakes—0
Understanding the Security of Deepfake Detection—0
Automated Deepfake Detection—0
FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning—0
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 Detection—0
An Examination of Fairness of AI Models for Deepfake Detection—0
DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning—0
Towards Measuring Fairness in AI: the Casual Conversations Dataset—0
KoDF: A Large-scale Korean DeepFake Detection Dataset—0
DeepFake-o-meter: An Open Platform for DeepFake Detection—0
Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward—0
The Deepfake Detection Dilemma: A Multistakeholder Exploration of Adversarial Dynamics in Synthetic Media—0
Adversarially robust deepfake media detection using fused convolutional neural network predictions—0
Fighting deepfakes by detecting GAN DCT anomalies—0
Exploring Adversarial Fake Images on Face Manifold—0
Contrastive Self-Supervised Learning of Global-Local Audio-Visual Representations—0
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All datasetsFakeAVCelebFaceForensicsDFDC1CIFAKE: Real and AI-Generated Synthetic ImagesCOCOFakeDFFDLAV-DF

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AV-Lip-Sync+Accuracy (%)99.29—Unverified
2AvtenetAccuracy (%)98.57—Unverified
3FACTORROC AUC97.4—Unverified
4RealForensicsROC AUC97.1—Unverified
5AVADROC AUC94.5—Unverified
6AV-Lip-Sync ModelAccuracy (%)94—Unverified
7FTCNROC AUC93.1—Unverified
8LipForensicsROC AUC91.1—Unverified
9Multimodal Ensemble ModelAccuracy (%)89—Unverified
10AD DFDROC AUC88.1—Unverified
#ModelMetricClaimedVerifiedStatus
1XceptionNetDF96.36—Unverified
2QAD-EAUC0.96—Unverified
3EfficientNetB4 + EfficientNetB4ST + B4Att + B4AttSTAUC0.94—Unverified
4MARLIN (ViT-L)AUC0.94—Unverified
5MARLIN (ViT-B)AUC0.93—Unverified
6MARLIN (ViT-S)AUC0.89—Unverified
7EfficientNetB4 + EfficientNetB4ST + B4AttSTLogLoss0.33—Unverified
#ModelMetricClaimedVerifiedStatus
1Cross Efficient Vision TransformerAUC0.95—Unverified
2Efficient Vision TransformerAUC0.92—Unverified
3EfficientNetB4 + EfficientNetB4ST + B4AttLogLoss0.46—Unverified
#ModelMetricClaimedVerifiedStatus
1STYLE0L99—Unverified
#ModelMetricClaimedVerifiedStatus
1FasterThanLiesAUC99.65—Unverified
#ModelMetricClaimedVerifiedStatus
1FasterThanLiesAUC1—Unverified
#ModelMetricClaimedVerifiedStatus
1FasterThanLiesAUC1—Unverified
#ModelMetricClaimedVerifiedStatus
1BA-TFDAUC0.99—Unverified