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2017

CycleGAN, a Master of Steganography

Chu, Casey, Zhmoginov, Andrey, Sandler, Mark

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CycleGAN (Zhu et al.

2017) is one recent successful approach to learn a transformation between two image distributions.
In a series of experiments, we demonstrate an intriguing property of the model: CycleGAN learns to "hide" information about a source image into the images it generates in a nearly imperceptible, high-frequency signal.
This trick ensures that the generator can recover the original sample and thus satisfy the cyclic consistency requirement, while the generated image remains realistic.

CycleGAN, a Master of Steganography · Around

Built on

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C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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A neural algorithm of artistic style
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L. A. Gatys, A. S. Ecker, and M. Bethge · 2015
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G. K. Dziugaite, Z. Ghahramani, and D. M. Roy · 2016
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