Increasing embedding capacity of stego images by exploiting edge pixels in prediction error space

In the field of data concealing, edge detection techniques are frequently employed, particularly for improving image quality and data security. These methods, however, have a lower embedding capacity. In order to take advantage of more edge pixels, many strategies are used nowadays. These schemes either combine the output from multiple edge detectors or enlarge the edges of an edge image by dilating. Even so, if the amount of data is vast, the techniques might not be able to conceal all of it. Therefore, a novel strategy for edge exploitation is still needed to regulate the effectiveness of edge detection-based data-hiding strategies. By using edge detectors in the prediction error space, we utilized more edge pixels in this study (PES). Applying a predictor on the cover image and then calculating the prediction errors, we prepared the PES. The edges in PES were then marked using the edge detector. The edge-error corresponding pixels received more information than the relevant pixels that did not create an edge-error. Additionally, we combined the results from different edge detectors to produce more edges, which does help to achieve a higher embedding capacity. We implanted $x$ number of secret bits in edge pixels and $y$ number of bits in non-edge pixels where $x>y$. The simulation results show that the proposed scheme outperforms its rivals on all performance-measuring criteria, including payload, stego image quality, and resistance to attack.