421,283 research outputs found
Steganographic Generative Adversarial Networks
Steganography is collection of methods to hide secret information ("payload")
within non-secret information "container"). Its counterpart, Steganalysis, is
the practice of determining if a message contains a hidden payload, and
recovering it if possible. Presence of hidden payloads is typically detected by
a binary classifier. In the present study, we propose a new model for
generating image-like containers based on Deep Convolutional Generative
Adversarial Networks (DCGAN). This approach allows to generate more
setganalysis-secure message embedding using standard steganography algorithms.
Experiment results demonstrate that the new model successfully deceives the
steganography analyzer, and for this reason, can be used in steganographic
applications.Comment: 15 pages, 10 figures, 5 tables, Workshop on Adversarial Training
(NIPS 2016, Barcelona, Spain
Inferring Sparsity: Compressed Sensing using Generalized Restricted Boltzmann Machines
In this work, we consider compressed sensing reconstruction from
measurements of -sparse structured signals which do not possess a writable
correlation model. Assuming that a generative statistical model, such as a
Boltzmann machine, can be trained in an unsupervised manner on example signals,
we demonstrate how this signal model can be used within a Bayesian framework of
signal reconstruction. By deriving a message-passing inference for general
distribution restricted Boltzmann machines, we are able to integrate these
inferred signal models into approximate message passing for compressed sensing
reconstruction. Finally, we show for the MNIST dataset that this approach can
be very effective, even for .Comment: IEEE Information Theory Workshop, 201
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