Adversarial Learning for Invertible Steganography

Deep neural networks have revolutionised the research landscape of steganography. However, their potential has not been explored in invertible steganography, a special class of methods that permits the recovery of distorted objects due to steganographic perturbations to their pristine condition. In...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.198425-198435
Main Author: Chang, Ching-Chun
Format: Article
Language:English
Subjects:
Artificial neural networks
Convolutional neural networks
Gallium nitride
Generative adversarial networks
Generators
Image coding
Image contrast
invertible steganography
Machine learning
Neural networks
Nonlinear distortion
Steganography
Training
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Summary:Deep neural networks have revolutionised the research landscape of steganography. However, their potential has not been explored in invertible steganography, a special class of methods that permits the recovery of distorted objects due to steganographic perturbations to their pristine condition. In this paper, we revisit the regular-singular (RS) method and show that this elegant but obsolete invertible steganographic method can be reinvigorated and brought forwards to modern generation via neuralisation . Towards developing a renewed RS method, we introduce adversarial learning to capture the regularity of natural images automatically in contrast to handcrafted discrimination functions based on heuristic image prior. Specifically, we train generative adversarial networks (GANs) to predict bit-planes that have been used to carry hidden information. We then form a synthetic image and use it as a reference to provide guidance on data embedding and image recovery. Experimental results showed a significant improvement over the prior implementation of the RS method based on large-scale statistical evaluations.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3034936