With the huge transfers of data involved in the modern world, it is both crucial and challenging to maintain the security of data. This paper proposes a novel algorithm of information embedding into digital images that could be used to protect confidential information. The presented algorithm makes use of the Chinese remainder theorem and adaptive embedding to achieve good imperceptibility along with the possibility of hiding a decent amount of confidential information. The algorithm is evaluated via computing experiments and evaluation results, as well as comparison with similar works, demonstrate good imperceptibility qualities of the proposed scheme.

Using an attack-simulation module is a well-recognized approach to improving the robustness of end-to-end neural-network-based data-hiding schemes. However, most proposed attack simulators are limited in the types of attacks they cover, usually handling only a basic set of digital transformations. Real, in-demand use cases for data-hiding methods may involve modifications that cannot be modeled by basic digital transformations such as filtering, noise, or compression. In the screen-cam scenario, when an image containing hidden data is displayed on a screen and captured by a camera, the distortions are much more complex and typically require manual experiments that manipulate physical objects in order to replicate. This hinders both the process of creating applicable data-hiding schemes for this scenario and evaluating their effectiveness. In this work, we propose a generator neural network to simulate screen-cam distortions that can replace the manual, time-consuming operations of replicating this attack in the real world, and we show how it can be used to improve the robustness of an existing data-hiding scheme. In our example, we increased robustness by 15% in terms of bit error rate.