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Conclusion (~1,700 words).
All backed up by over 200 references (~6,500 words).
We must stop crediting the wrong people for inventions made by others.
Instead let s heed the recent call in the journal
Nature: Let 2020 be the year in which we value those who ensure that
science is self-correcting [SV20].
Like those who know me can testify, finding and citing original sources of scientific and technological innovations is important to me, whether they are mine or other people s [DL1][DL2][HIN][NASC1-9]. The present page is offered as a resource for computer scientists who share this inclination.
By grounding research in its true intellectual foundations and crediting the original inventors,
(Image: Getty Images)
Not sure if the video you re watching is the real thing or a deepfake? Take a good, long look at the eyes.
According to computer scientists at the University of Buffalo, light reflections in the eye are the key to deciphering whether the person you re watching in a given image is genuine or a clever deepfake.
There s a special tool that can automatically identify deepfake photos by analyzing light reflections in the subject s eyes. When used on portrait-like photos across a series of experiments, the tool achieved 94% efficacy when sussing out real photos from fakes.
Experiments using the tool were written up in a paper accepted at the IEEE International Conference on Acoustics, Speech, and Signal Processing, which takes place in June in Toronto. The paper, Exposing GAN-Generated Faces Using Inconsistent Corneal Specular Highlights, refers to generative adversary network (GAN) images, including those created by AI.
Published 15 March 2021
Computer scientists have developed a tool that automatically identifies deepfake photos by analyzing light reflections in the eyes. The tool proved 94 percent effective with portrait-like photos in experiments.
University at Buffalo computer scientists have developed a tool that automatically identifies deepfake photos by analyzing light reflections in the eyes.
The tool proved 94 percent effective with portrait-like photos in experiments described in a paper accepted at the IEEE International Conference on Acoustics, Speech and Signal Processing to be held in June in Toronto, Canada.
“The cornea is almost like a perfect semisphere and is very reflective,” says the paper’s lead author, Siwei Lyu, PhD, SUNY Empire Innovation Professor in the Department of Computer Science and Engineering. “So, anything that is coming to the eye with a light emitting from those sources will have an image on the cornea.
TechShout
A team of researchers has developed a tool that automatically identifies deepfake photos by analyzing light reflections in the eyes.
The tool proved 94 per cent effective in experiments described in a paper accepted at the IEEE International Conference on Acoustics, Speech and Signal Processing.
“The cornea is almost like a perfect semisphere and is very reflective,” said the lead author, Siwei Lyu from University at Buffalo.
“So, anything that is coming to the eye with a light emitting from those sources will have an image on the cornea. The two eyes should have very similar reflective patterns because they’re seeing the same thing. It’s something that we typically don’t typically notice when we look at a face,” Lyu, added.