  For as long as humans have participated in the act of communication,
concealing information in those communicative mediums has manifested into an
art of its own. Crytographic messages, through written language or images, are
a means of concealment, usually reserved for highly sensitive or compromising
information. Specifically, the field of Cryptography is the construction and
analysis of protocols that prevent third parties from understanding private
messages. Steganography is related to Cryptography in that the goal is to
obscure information using some method or algorithm, but the most important
difference is that the information and the method of concealing information
within Steganography both involve images--more precisely, the embedding of one
image or piece of information into another image. Ever since the creation of
covert communication methods, steps have been taken to crack cryptography and
steganography algorithms. The desire for this rises from both human curiosity
and the need to counteract adverse uses, such as encoding harmful media in
inconspicuous media (phishing attack). In this paper, we succeed in cracking
the Least Significant Bit (LSB) steganography algorithm using Cycle Generative
Adversarial Networks (CycleGANs) and Bayesian Optimization and compare the use
of CycleGANs against Convolutional Autoencoders. The results of our experiments
highlight the promising nature of CycleGANs in cracking steganography and open
several possible avenues of research.
