  Image steganography aims to securely embed secret information into cover
images. Until now, adaptive embedding algorithms such as S-UNIWARD or Mi-POD,
are among the most secure and most used methods for image steganography. With
the arrival of deep learning and more specifically the Generative Adversarial
Networks (GAN), new techniques have appeared. Among these techniques, there is
the 3 player game approaches, where three networks compete against each
other.In this paper, we propose three different architectures based on the 3
player game. The first-architecture is proposed as a rigorous alternative to
two recent publications. The second takes into account stego noise power.
Finally, our third architecture enriches the second one with a better
interaction between the embedding and extracting networks. Our method achieves
better results compared to the existing works GSIVAT, HiDDeN, and paves the way
for future research on this topic.
