Bayesian Neural Networks for Reversible Steganography
- URL: http://arxiv.org/abs/2201.02478v1
- Date: Fri, 7 Jan 2022 14:56:33 GMT
- Title: Bayesian Neural Networks for Reversible Steganography
- Authors: Ching-Chun Chang
- Abstract summary: We propose to consider uncertainty in predictive models based upon a theoretical framework of Bayesian deep learning.
We approximate the posterior predictive distribution through Monte Carlo sampling with reversible forward passes.
We show that predictive uncertainty can be disentangled into aleatoric uncertainties and these quantities can be learnt in an unsupervised manner.
- Score: 0.7614628596146599
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances in deep learning have led to a paradigm shift in reversible
steganography. A fundamental pillar of reversible steganography is predictive
modelling which can be realised via deep neural networks. However, non-trivial
errors exist in inferences about some out-of-distribution and noisy data. In
view of this issue, we propose to consider uncertainty in predictive models
based upon a theoretical framework of Bayesian deep learning. Bayesian neural
networks can be regarded as self-aware machinery; that is, a machine that knows
its own limitations. To quantify uncertainty, we approximate the posterior
predictive distribution through Monte Carlo sampling with stochastic forward
passes. We further show that predictive uncertainty can be disentangled into
aleatoric and epistemic uncertainties and these quantities can be learnt in an
unsupervised manner. Experimental results demonstrate an improvement delivered
by Bayesian uncertainty analysis upon steganographic capacity-distortion
performance.
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