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Research: new deepfake detector designed to be less biased
With facial recognition performing worse on certain races and genders, algorithms developed at UB close the gap.
Deepfake detection algorithms often perform differently across races and genders, including a higher false positive rate on Black men than on white women. UB computer scientist and deepfake expert Siwei Lyu created a photo collage out of the hundreds of faces that his detection algorithms had incorrectly classified as fake — and the new composition clearly had a predominantly darker skin tone. First, their demographic-aware method supplied algorithms with datasets that labeled subjects’ gender — male or female — and race — white, Black, Asian or other — and instructed it to minimize errors on the less-represented groups.
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