Mobile and digital media are getting more and more popular on Internet and cloud services. In social networks, many multi-media such as images, videos, and audios are produced by different aspects of the human activities. Moreover, telemedicine applies telecommunication and information technology to offer clinical health care from a distance. In critical care and emergency situations, health informatics, medical, and imaging data are transmitted from doctors and healthcare professionals to discuss patient issues. Therefore, E-Healthcare data management plays a crucial role in modern hospitals. Over Internet and cloud services, digitized healthcare systems have provided easy access, viewing and sharing of digitized medical images to patients, doctors, medical professionals, health care providers, and institutes of medicine. Medical images may provide for teleconferences among clinicians, interdisciplinary exchanges between radiologists for consultative purposes, and distant learning of medical personnel. For illegal purposes used for insurance, for example, digital medical malignant nodule images may be modified on lung parenchyma in chest CT scan images. Therefore, data hiding schemes (Wang et al.
; Huang et al.
; Li et al.
; Wang et al.
) are much simpler and cheaper than an intrusion detection system (Huang and Hwang, 2012
) to protect security and privacy of images by image with an unique digital identity.
Reversible data hiding schemes (Li et al.
; Huang et al.
; Chen and Guo, 2020
; Kim et al.
) (RDH) are applied to embed secrets inside an image as a stego-image with minimal distortion. After the secrets are retrieved from a stego-image at the extraction stage, the original images can be reconstructed exactly. The major applications of RDH are authentication, diagnostic image, military imagery, astronomical images, satellite, and artwork preservation. Shi et al.
) classified RDH techniques into six categories: histogram shifting, image compressing (e.g., JPEG), semi-fragile authentication, image contrast enhancement, encrypted images, and RDH based on audio and video. In general, there are two types of popular RDH techniques: histogram shifting (HS) (Thodi and Rodriguez, 2007
; Hong et al.
) and difference expansion (DE). In 2006, Ni et al.
) proposed the first HS-based RDH by modifying the generated histogram. Some extensions of Ni et al.’s HS-based RDH methods proposed such as block-based HS (Fallahpour and Sedaaghi, 2007
), difference-histogram (Lee et al.
), high-frequency IWT coefficients (Xuan et al.
; Huang et al.
). In 2002, Tian (2002
) proposed a high capacity DE method. Furthermore, DE has been developed in three species: integer-to-integer transformation (Qiu et al.
), prediction-error expansion (PEE) (Dragoi and Coltuc, 2015
), and adaptive embedding (Hong et al.
). There are three approaches in image compressing: RDH with quantized DCT coefficients modification (Huang et al.
), RDH with quantization table modification (Wang et al.
), RDH with Huffman table modification (Wu and Deng, 2011
). In 2003, Vleeschouwer et al.
) presented the first robust RDH based on the correlations among the neighbouring pixels. In general, the approaches of contrast enhancement RDH applied some functionalities such as histogram bin expansion operations to improve the visual quality. There are four RDH approaches with contrast enhancement: method by histogram bin expansion (Wu et al.
), method with contrast enhancement for medical images (Wu et al.
), method with the controlled contrast enhancement (Gao and Shi, 2015
), and automatic contrast enhancement method (Kim et al.
). Due to protection of the privacy of data and enabling the cloud server to easily manage the data, more and more researchers study reversible data hiding in encrypted images. The techniques of encrypted images are divided into three types: vacating room before encryption (Cao et al.
), vacating room after encryption (Zhang, 2011
), and reversible image transformation (Zhang et al.
Considering resource sharing data in Cloud computing environments, stego-images may be disturbed by various techniques such as a chaotic Hénon function to obtain encrypted images (Huang et al.
). Abbasy and Shanmugam (2011
) presented biological aspects of the DNA to increase the level of data confidentiality among clients. Some other authors (Zhang, 2012
; Surekha and Swamy, 2013
) converted stego-images into encryption data transferred to public Cloud environments. Based on prediction error expansion, a predictive value is calculated by predictors. Then the secret bit-stream will be embedded into the cover image according to the expansion of the difference between a pixel and its predictive values.
Most of image data hiding techniques (Huang et al.
; Wu et al.
; Zhang S. et al.
; Jana et al.
) are developed in 2D images. However, many medical images are produced and processed (Tseng and Huang, 2010
) as stacks of slices such as CT, MRI, and PET. These slices can be used to generate 3D image volumetric information. Thus, it is important to apply data hiding in 3D images efficiently. Recently, for digital media it is getting more and more important to apply data hiding to the quality of compressed video. Shanableh (2012
) applied data hiding schemes to embed secrets into a compressed video bit stream for copyright protection.
In this paper, two-tier structures, histogram-shifting of median and prediction error, are utilized to embed secret messages. The rest of this paper is presented as follows. The second section demonstrates our method. The fourth section shows the outcome of the experiments. Conclusions are presented in the last section.