Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Less is More: Reversible Steganography with Uncertainty-Aware Predictive
  Analytics   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 and the pivot of which is the predictive module. The function of this
module is to predict pixel intensity 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 analyses the predictive uncertainty and endows
the predictive module with the option to abstain when encountering a high level
of uncertainty. Uncertainty analysis can be formulated as a pixel-level binary
classification problem and tackled by both supervised and unsupervised
learning. In contrast to handcrafted statistical analytics, learning-based
analytics can learn to follow some general statistical principles and
simultaneously adapt to a specific predictor. Experimental results show that
steganographic performance can be remarkably improved by adaptively filtering
out the unpredictable regions with the learning-based uncertainty analysers.

28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph