A new study published in the Proceedings of the National Academy of Sciences USA provides a measure of how far [deepfake] technology has progressed. The results suggest that real humans can easily fall for machine-generated faces—and even interpret them as more trustworthy than the genuine article.
“We found that not only are synthetic faces highly realistic, they are deemed more trustworthy than real faces,” says study co-author Hany Farid, a professor at the University of California, Berkeley. The result raises concerns that “these faces could be highly effective when used for nefarious purposes.”
The synthetic faces for this study were developed in back-and-forth interactions between two neural networks, examples of a type known as generative adversarial networks. One of the networks, called a generator, produced an evolving series of synthetic faces like a student working progressively through rough drafts. The other network, known as a discriminator, trained on real images and then graded the generated output by comparing it with data on actual faces.
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The group rating trustworthiness gave the synthetic faces a slightly higher average rating of 4.82, compared with 4.48 for real people.
The researchers were not expecting these results. “We initially thought that the synthetic faces would be less trustworthy than the real faces,” says study co-author Sophie Nightingale.
