Data Hiding under Content-Aware to Trade-Off Between Embedding Quality of Image

Abstract

There are many ways to hide data, such as data hiding, disparity expansion-based data hiding, histogram hiding. In this paper, four ways to hide data for different applications are suggested by making improvements to current methods. By putting data in certain parts of an image, these methods improve the quality of images over what was already possible. Then, give a trade-off between embedding capacity and visual clarity. Also, think about hiding content information while hiding data.The third method, called "Uniform Region-based Histogram Shift (URHS) data hiding technique," is used to hide data in areas where pixels have the most important values. The fourth method is called Reference Image and Edge Detection (RIE), and it is used to hide data in edges. The problems with the current DE-based data hiding method are fixed by the TLFCN-based method. This TLFCN method for hiding data takes longer than other methods for hiding data. The projected URHS data hiding technique saves time compared to the TLFCN data hiding technique and solves the problem of flipped pixels in the literature HM methods. Most of the known ways to hide data, like DE, HM, and Edge detection, are prone to the flattening effect. The results of experiments show that the projected approaches work better than the ones that are already in use for standard grayscale images and the USC SIPI image database. Techniques that have been suggested also offer trade-offs between embedding capacity and visual clarity, and they take cover content information into account while hiding data.