DeepFake Detection
133 papers with code • 5 benchmarks • 18 datasets
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
Benchmarks
These leaderboards are used to track progress in DeepFake Detection
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Libraries
Use these libraries to find DeepFake Detection models and implementationsDatasets
FaceForensics++
Celeb-DF
DFDC
FaceForensics
FakeAVCeleb
WildDeepfake
DeeperForensics-1.0
WaveFake
Latest papers
Preserving Fairness Generalization in Deepfake Detection
purdue-m2/fairness-generalization
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The existing method for addressing this problem is providing a fair loss function.
CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection
sohailahmedkhan/CLIPping-the-Deception
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The recent advancements in Generative Adversarial Networks (GANs) and the emergence of Diffusion models have significantly streamlined the production of highly realistic and widely accessible synthetic content.
Deepfake Detection and the Impact of Limited Computing Capabilities
PalomaCantero/Deepfake-Detection-and-the-Impact-of-Limited-Computing-Capabilities
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The rapid development of technologies and artificial intelligence makes deepfakes an increasingly sophisticated and challenging-to-identify technique.
Lips Are Lying: Spotting the Temporal Inconsistency between Audio and Visual in Lip-Syncing DeepFakes
aaroncomo/lipfd
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In recent years, DeepFake technology has achieved unprecedented success in high-quality video synthesis, whereas these methods also pose potential and severe security threats to humanity.
Exposing Lip-syncing Deepfakes from Mouth Inconsistencies
A lip-syncing deepfake is a digitally manipulated video in which a person's lip movements are created convincingly using AI models to match altered or entirely new audio.
Frequency Masking for Universal Deepfake Detection
chandlerbing65nm/FakeImageDetection
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We study spatial and frequency domain masking in training deepfake detectors.
Linguistic Profiling of Deepfakes: An Open Database for Next-Generation Deepfake Detection
dflip3k/dflip-3k
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The two distinguished features enable DFLIP-3K to develop a benchmark that promotes progress in linguistic profiling of deepfakes, which includes three sub-tasks namely deepfake detection, model identification, and prompt prediction.
Unmasking Deepfake Faces from Videos Using An Explainable Cost-Sensitive Deep Learning Approach
To create a reliable deepfake detection system, four pre-trained Convolutional Neural Network (CNN) models: XceptionNet, InceptionResNetV2, EfficientNetV2S, and EfficientNetV2M were used.
Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection
Recently, the proliferation of highly realistic synthetic images, facilitated through a variety of GANs and Diffusions, has significantly heightened the susceptibility to misuse.
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
cecile-hi/regularized-adaptive-weight-modification
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The rapid evolution of speech synthesis and voice conversion has raised substantial concerns due to the potential misuse of such technology, prompting a pressing need for effective audio deepfake detection mechanisms.
