  We propose a method to improve steganography by increasing the resilience of
stego-media to discovery through steganalysis. Our approach enhances a class of
steganographic approaches through the inclusion of a steganographic assistant
convolutional neural network (SA-CNN). Previous research showed success in
discovering the presence of hidden information within stego-images using
trained neural networks as steganalyzers that are applied to stego-images. Our
results show that such steganalyzers are less effective when SA-CNN is employed
during the generation of a stego-image. We also explore the advantages and
disadvantages of representing all the possible outputs of our SA-CNN within a
smaller, discrete space, rather than a continuous space. Our SA-CNN enables
certain classes of parametric steganographic algorithms to be customized based
on characteristics of the cover media in which information is to be embedded.
Thus, SA-CNN is adaptive in the sense that it enables the core steganographic
algorithm to be especially configured for each particular instance of cover
media. Experimental results are provided that employ a recent steganographic
technique, S-UNIWARD, both with and without the use of SA-CNN. We then apply
both sets of stego-images, those produced with and without SA-CNN, to an
exmaple steganalyzer, Yedroudj-Net, and we compare the results. We believe that
this approach for the integration of neural networks with hand-crafted
algorithms increases the reliability and adaptability of steganographic
algorithms.

Cryptography and Security;Machine Learning;Multimedia