31,962 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
Bidirectional Conditional Generative Adversarial Networks
Conditional Generative Adversarial Networks (cGANs) are generative models
that can produce data samples () conditioned on both latent variables ()
and known auxiliary information (). We propose the Bidirectional cGAN
(BiCoGAN), which effectively disentangles and in the generation process
and provides an encoder that learns inverse mappings from to both and
, trained jointly with the generator and the discriminator. We present
crucial techniques for training BiCoGANs, which involve an extrinsic factor
loss along with an associated dynamically-tuned importance weight. As compared
to other encoder-based cGANs, BiCoGANs encode more accurately, and utilize
and more effectively and in a more disentangled way to generate
samples.Comment: To appear in Proceedings of ACCV 201
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