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Limits of Deepfake Detection: A Robust Estimation Viewpoint (1905.03493v1)
Published 9 May 2019 in cs.LG, cs.AI, cs.IT, math.IT, and stat.ML
Abstract: Deepfake detection is formulated as a hypothesis testing problem to classify an image as genuine or GAN-generated. A robust statistics view of GANs is considered to bound the error probability for various GAN implementations in terms of their performance. The bounds are further simplified using a Euclidean approximation for the low error regime. Lastly, relationships between error probability and epidemic thresholds for spreading processes in networks are established.
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