Contributed Talk: Limits of Deepfake Detection: A Robust Estimation Viewpoint
2019 Oral Presentation
in
Workshop: Synthetic Realities: Deep Learning for Detecting AudioVisual Fakes
in
Workshop: Synthetic Realities: Deep Learning for Detecting AudioVisual Fakes
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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