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Sumsub launches a free tool to detect deepfakes and synthetic fraud

Sumsub launches a free tool to detect deepfakes and synthetic fraud
 

Sumsub has announced the release of For Fake’s Sake, a set of models enabling the detection of deepfakes and synthetic fraud in visual assets. It is the first solution developed by a verification provider and made available to all for free. For Fake’s Sake helps users estimate how likely it is that an uploaded image was created artificially.

Sumsub’s AI/ML Research Lab developed For Fake’s Sake—a set of four distinct machine learning-driven models for deepfake and synthetic fraud detection. This release presents an experimental technical strategy to help people responsibly engage with AI-generated content. This tool also has the potential to adapt and grow with other AI-driven tools. Following this initial contribution, Sumsub will leverage feedback from the AI research community to improve the models’ capabilities further.

“We’ve dedicated substantial efforts to combat the menace of deepfakes and the adverse impacts of synthetic fraud by constantly upgrading our existing solutions and developing new features, including the newly enhanced deepfake detector of our liveness tool,” comments Vyacheslav Zholudev, co-founder and CTO of Sumsub.

According to Sumsub’s most recent internal data, the first half of 2023 saw a considerable outbreak of deepfake cases worldwide compared to the second half of 2022. Deepfakes increased by 84 percent in Great Britain, 250 percent in the U.S., more than 300 percent in Germany and Italy and 500 percent in France. AI-generated identity fraud cases were noticed in Australia (1300 percent), Vietnam (1400 percent), and Japan (2300 percent).

“As AI technologies advance, we foresee a tightening of regulations governing their use. For example, it may soon become mandatory to apply watermarks to all synthetic images. However, fraudsters will continually seek ways to overcome regulations,” explains Pavel Goldman-Kalaydin, head of AI/ML at Sumsub. “We’re pleased to share our experimental synthetic image detection models as a benchmark for further development in the battle against AI-generated fraud.”

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