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The Ethereal
Limits of Deepfake Detection: A Robust Estimation Viewpoint
May 09, 2019 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Sakshi Agarwal, Lav R. Varshney
arXiv ID
1905.03493
Category
cs.LG: Machine Learning
Cross-listed
cs.AI,
cs.IT,
stat.ML
Citations
49
Venue
arXiv.org
Last Checked
5 months ago
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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