Adversarial Learning for Invertible Steganography

44Citations
Citations of this article
27Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

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.

Cite

CITATION STYLE

APA

Chang, C. C. (2020). Adversarial Learning for Invertible Steganography. IEEE Access, 8, 198425–198435. https://doi.org/10.1109/ACCESS.2020.3034936

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free