562 research outputs found
Deep Learning for Reversible Steganography: Principles and Insights
Deep-learning\textendash{centric} reversible steganography has emerged as a
promising research paradigm. A direct way of applying deep learning to
reversible steganography is to construct a pair of encoder and decoder, whose
parameters are trained jointly, thereby learning the steganographic system as a
whole. This end-to-end framework, however, falls short of the reversibility
requirement because it is difficult for this kind of monolithic system, as a
black box, to create or duplicate intricate reversible mechanisms. In response
to this issue, a recent approach is to carve up the steganographic system and
work on modules independently. In particular, neural networks are deployed in
an analytics module to learn the data distribution, while an established
mechanism is called upon to handle the remaining tasks. In this paper, we
investigate the modular framework and deploy deep neural networks in a
reversible steganographic scheme referred to as prediction-error modulation, in
which an analytics module serves the purpose of pixel intensity prediction. The
primary focus of this study is on deep-learning\textendash{based} context-aware
pixel intensity prediction. We address the unsolved issues reported in related
literature, including the impact of pixel initialisation on prediction accuracy
and the influence of uncertainty propagation in dual-layer embedding.
Furthermore, we establish a connection between context-aware pixel intensity
prediction and low-level computer vision and analyse the performance of several
advanced neural networks
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