An Overview on Image Segmentation Techniques for Reversible Data Hiding

The fields of image processing and computer vision have witnessed significant growth due to the proliferation of digital images across diverse domains. Image Segmentation is the fundamental task in digital image processing, finding applications in pivotal areas such as medical imaging, covert communication, autonomous driving, satellite imaging, among others. One particularly intriguing application of image segmentation lies in Reversible Data Hiding (RDH), where the delineation of the main Region of Interest (ROI) and Non-Region of Interest (NROI) using segmentation plays a crucial role for effective data encryption in the images. Over the last two decades, various studies focussed on developing an efficient data hiding approach, which can embed secret data within ROI and NROI part of image while ensuring its quality. A comprehensive survey has been conducted that meticulously examines different segmentation techniques, along with its usage in reversible data hiding. The main objective of this survey is to compare the performance metrics of reversible data hiding after applying different image segmentation techniques. The image segmentation techniques have been categorized systematically into three main classes: i) Traditional segmentation techniques, encompassing a spectrum of approaches like thresholding, region-based and edge detection based techniques, ii) Machine Learning (ML) based approach consisting of Clustering, Support Vector Machine (SVM) and iii) Deep Learning (DL) based technique, propelled by Convolutional Neural Networks (CNNs) that have emerged as a transformative paradigm, revolutionizing segmentation tasks with their ability to learn complex images. The survey finds out that PSNR value of data embedded images is high after applying deep learning based segmentation technique.