On the predictability in reversible steganography

TL;DR

This paper explores how neural network-based predictability analysis can enhance reversible steganography by improving data embedding capacity and imperceptibility through adaptive prediction techniques.

Contribution

It introduces learning-based predictability frameworks for reversible steganography, surpassing traditional statistical methods in optimizing embedding performance.

Findings

Learning-based predictability analysis improves steganographic capacity.

Adaptive embedding enhances imperceptibility.

Neural network predictors outperform conventional methods.

Abstract

Artificial neural networks have advanced the frontiers of reversible steganography. The core strength of neural networks is the ability to render accurate predictions for a bewildering variety of data. Residual modulation is recognised as the most advanced reversible steganographic algorithm for digital images. The pivot of this algorithm is predictive analytics in which pixel intensities are predicted given some pixel-wise contextual information. This task can be perceived as a low-level vision problem and hence neural networks for addressing a similar class of problems can be deployed. On top of the prior art, this paper investigates predictability of pixel intensities based on supervised and unsupervised learning frameworks. Predictability analysis enables adaptive data embedding, which in turn leads to a better trade-off between capacity and imperceptibility. While conventional methods estimate predictability by the statistics of local image patterns, learning-based frameworks consider further the degree to which correct predictions can be made by a designated predictor. Not only should the image patterns be taken into account but also the predictor in use. Experimental results show that steganographic performance can be significantly improved by incorporating the learning-based predictability analysers into a reversible steganographic system.