  In this work, we mainly study the mechanism of learning the steganographic
algorithm as well as combining the learning process with adversarial learning
to learn a good steganographic algorithm. To handle the problem of embedding
secret messages into the specific medium, we design a novel adversarial modules
to learn the steganographic algorithm, and simultaneously train three modules
called generator, discriminator and steganalyzer. Different from existing
methods, the three modules are formalized as a game to communicate with each
other. In the game, the generator and discriminator attempt to communicate with
each other using secret messages hidden in an image. While the steganalyzer
attempts to analyze whether there is a transmission of confidential
information. We show that through unsupervised adversarial training, the
adversarial model can produce robust steganographic solutions, which act like
an encryption. Furthermore, we propose to utilize supervised adversarial
training method to train a robust steganalyzer, which is utilized to
discriminate whether an image contains secret information. Numerous experiments
are conducted on publicly available dataset to demonstrate the effectiveness of
the proposed method.
