Recently, generative adversarial network is the hotspot in research areas and industrial application areas. Its application on data generation in computer vision is most common usage. This paper extends its application to data hiding and security area. In this paper, we propose the novel framework to integrate steganography and steganalysis processes. The proposed framework applies generative adversarial networks as the core structure. The discriminative model simulate the steganalysis process, which can help us understand the sensitivity of cover images to semantic changes. The steganography generative model is to generate stego image which is aligned with the original cover image, and attempts to confuse steganalysis discriminative model. The introduction of cycle discriminative model and inconsistent loss can help to enhance the quality and security of generated stego image in the iterative training process. Training dataset is mixed with intact images as well as intentional attacked images. The mix training process can further improve the robustness and security of new framework. Through the qualitative, quantitative experiments and analysis, this novel framework shows compelling performance and advantages over the current state-of-the-art methods in steganography and steganalysis benchmarks.
The paper is on applying GAN to steganography, and it is especially suited for people who does not know steganography. In fact, 1/4 of the paper is spent on introducing (in a very clear and catchy way) the basics. State of the art section nonetheless shows that the authors are grounded with the very last related work. The novel GAN framework is called ISS-GAN. Simply speaking, ISS-GAN is built by the combo: <steganography generative, steganalysis discriminative>. In the details, the implementation is not straightforward, and embeds interesting ideas. Two generative and two discriminative models are intertwined: one generative for the stego image generation process, the other generative for the secret image extraction process. The two discriminative models are for ensuring that the distribution of cover images is indistinguishable from the stego distribution and to ensure that the distribution of extracted secret images is indistinguishable from the distribution of the original secret images. In rough terms, the adversarial double loop serves to ensure that the embedding and the extraction function preserve both the aspect of the original and the secret images. PSNR and SSIM metrics are employed to give a quantitative check of the approach. 
Obvious questions are about the scalability of the approach, since it applies to 256x256 miniatures, which act well on the figures of an ICLR paper but poor in reality. At the same time, is it reasonable to show images such that Figure 3? It is obvious stega processes are now very effective, and stressing that by showing seemingly same images it is not very illustrative. Figure 4 typos are reated to row column ordering, but even in this case is hard to spot something without spending time in magnifying the pdf playing at look at the differences… Figure 5 and 6 are even worse, with tens of practically equal images. Less images with differences pictures should have been better. In addition, with these qualitative results, it is hard to give a weight to the quantitative comparisons, in terms of both the PSNR and SSIM metrics.
The most obvious and perhaps elementary question I have is how risky is that the ISS GAN model can be replicated by an attacker which sniffs the secret images by simply having a training set which approximatively is the same of the primary sender?
The other questions are about how much realistic is that the images are affected by the noises of Table 5, nowadays?