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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 471–480 of 580 papers

Title	Date	Tasks	Status	Hype
Robust Deepfake On Unrestricted Media: Generation And Detection	Feb 13, 2022	DeepFake DetectionFace Swapping	—Unverified	0
A Review of Deep Learning-based Approaches for Deepfake Content Detection	Feb 12, 2022	DeepFake DetectionDeep Learning	—Unverified	0
D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles	Feb 11, 2022	Adversarial RobustnessDeepFake Detection	—Unverified	0
FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbations	Feb 7, 2022	DeepFake DetectionFace Swapping	—Unverified	0
Block shuffling learning for Deepfake Detection	Feb 6, 2022	DeepFake DetectionFace Detection	CodeCode Available	0
Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection	Jan 18, 2022	DeepFake Detection	CodeCode Available	1
TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations	Dec 16, 2021	DeepFake DetectionDetecting Image Manipulation	CodeCode Available	1
ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images	Dec 7, 2021	DeepFake DetectionFace Swapping	CodeCode Available	0
Contrastive Learning of Global and Local Video Representations	Dec 1, 2021	ClassificationContrastive Learning	—Unverified	0
A War Beyond Deepfake: Benchmarking Facial Counterfeits and Countermeasures	Nov 25, 2021	BenchmarkingDeepFake Detection	—Unverified	0

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All datasets FakeAVCeleb FaceForensics DFDC 1 CIFAKE: Real and AI-Generated Synthetic Images COCOFake DFFD LAV-DF

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	AV-Lip-Sync+	Accuracy (%)	99.29	—	Unverified
2	Avtenet	Accuracy (%)	98.57	—	Unverified
3	FACTOR	ROC AUC	97.4	—	Unverified
4	RealForensics	ROC AUC	97.1	—	Unverified
5	AVAD	ROC AUC	94.5	—	Unverified
6	AV-Lip-Sync Model	Accuracy (%)	94	—	Unverified
7	FTCN	ROC AUC	93.1	—	Unverified
8	LipForensics	ROC AUC	91.1	—	Unverified
9	Multimodal Ensemble Model	Accuracy (%)	89	—	Unverified
10	AD DFD	ROC AUC	88.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	XceptionNet	DF	96.36	—	Unverified
2	QAD-E	AUC	0.96	—	Unverified
3	EfficientNetB4 + EfficientNetB4ST + B4Att + B4AttST	AUC	0.94	—	Unverified
4	MARLIN (ViT-L)	AUC	0.94	—	Unverified
5	MARLIN (ViT-B)	AUC	0.93	—	Unverified
6	MARLIN (ViT-S)	AUC	0.89	—	Unverified
7	EfficientNetB4 + EfficientNetB4ST + B4AttST	LogLoss	0.33	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Cross Efficient Vision Transformer	AUC	0.95	—	Unverified
2	Efficient Vision Transformer	AUC	0.92	—	Unverified
3	EfficientNetB4 + EfficientNetB4ST + B4Att	LogLoss	0.46	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	STYLE	0L	99	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FasterThanLies	AUC	99.65	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FasterThanLies	AUC	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FasterThanLies	AUC	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BA-TFD	AUC	0.99	—	Unverified