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Deepfake can be recognized by incorrect reflection in people’s eyes
13.03.2021 [14:19],
Gennady Detinich

Researchers at the University of Buffalo have developed a tool for near-error-free detection of deepfakes on digital images of people. The new algorithm is wrong only six times out of a hundred. This can help combat false information, but the algorithm has a number of limitations.

Analysis of the reflection in the eyes showed that all these people are not real. Image source: University at Buffalo

Researchers’ proposed algorithm explores reflections in people’s eyes in digital photographs. In real images, reflections in the eyes are usually the same, while neural networks in the process of creating a deepfake operate on many digital images of people from an extensive database, which leads to the synthesis of different reflections on one and the other cornea of ​​the eye.

In particular, analysis of the database of images of fake people generated by the StyleGAN2 neural network showed that the algorithm is able to detect a deepfake in 94% of cases of portrait images of people made with good lighting and with a sufficiently high resolution. At the same time, the accuracy dropped rapidly if the resolution was reduced or the illumination was insufficient to form a reflection from the cornea.

It should also be recognized that under certain lighting conditions, the reflections in the eyes may differ, just as this algorithm cannot be applied for cases when the second eye is not visible or is in the shadow. However, any progress on the path of exposing deepfakes is welcome. Over time, the spread of counterfeit digital audiovisual information promises to be a serious problem.

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