Artificially generated ‘deepfake’ video, image, audio and text pose a major challenge for governments, politicians, companies, CEOs, celebrities, academics and commentators.

A growing number of technology companies, academic researchers and NGOs are developing ways to identify false and misleading AI-generated media. Progress is being made, but there is a long way to go until a viable, comprehensive solution, or set of solutions, are available.

Here is a (non-comprehensive) list of deepfake datasets, toolkits and tools on the market and in the pipeline:

Adobe Photoshop Face Aware Liquify detectorAdobe/UC BerkeleyPhotoshop imagedetection
ADVBOX (pdf)Baidu X Labmultipledetection
Amber Detect Ambervideodetection
AssemblerJigsaw/Google ResearchStyleGAN video detection
DeeperForensics 1.0 (pdf)SenseTime Research/Nanyang Technological Universityfacial imagedataset
Deepstar ZeroFoxvideotoolkit
DeeptraceDeeptrace Labsvideomonitoring, detection, mitigation
DeepFake DetectionDessavideodataset
Deep VoicePindropvoicemonitoring, detection, mitigation
DiggerDW Innovation/iLab ATC/Fraunhofer IDMTaudiodetection
FaceForensics++ Technical University of Munich/ University Federico II of Naplesfacial imageanalysis
Grover Allen Institute for AInewsdetection
GLTRMIT/IBM Watson AI Lab/Harvard NLPtextdetection
Media ForensicsDARPA MediForimage, videodetection
Reality DefenderAI Foundationimage detection
ResemblyzerResemble AIaudiodetection
SherlockSherlock AIvideodetection
Trusted Media CaptureSerelayimage, videodetection
Truepic VisionTruepicphoto and videodetection
Visual deepfakes Jigsaw/Google Research imagedataset

Last updated February 24, 2020

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