ABSTRACT Reversible data hiding in encrypted images (RDH‐EI) is a key technique for secure communication, secure cloud storage and privacy protection. Most existing RDH‐EI algorithms rely on a single and static bit‐plane encoding strategy, failing to fully exploit the structural differences of bit‐planes from the block level to the sequence level, thereby limiting further improvements on embedding capacity. To address this issue, this paper proposes a novel RDH‐EI algorithm based on Bit‐plane Classification and Adaptive Group Coding (hereafter BCAGC algorithm). The core contributions are as follows: (1) A bit‐plane classification mechanism is proposed. It categorises bit‐planes into simple or complex types according to the number of non‐all‐zero 4‐bit sequence (NAZ‐4BS) patterns. (2) An adaptive group coding scheme is proposed. It dynamically selects between fixed‐length coding and Huffman coding based on bit‐plane types and the frequency distribution of NAZ‐4BS patterns, thereby achieving compact coding tables and efficient bit‐plane compression. Experimental results demonstrate that the proposed BCAGC algorithm achieves average embedding rates of 4.1472, 4.0565 and 3.4465 bpp on the BOSSbase, BOWS‐2 and UCID datasets, respectively, outperforming several state‐of‐the‐art RDH‐EI methods.

ABSTRACT Reversible data hiding in encrypted images (RDHEI) enables secret data embedding within encrypted images while allowing for the lossless recovery of the original image after data extraction. This technique holds significant applications in various domains such as cloud storage and data security. However, many existing RDHEI methods suffer from limited embedding capacity. To address this limitation, we present a novel and high capacity RDHEI algorithm via bit‐plane block rearrangement and intra‐block compression coding (hereafter BRBCC algorithm). First, the prediction error (PE) image is generated by using a median edge detection predictor, and the high‐order zero‐valued bit‐planes are compressed. The non‐zero‐valued bit‐planes are then separated into non‐overlapping blocks that can be classified as all‐zero blocks, embeddable blocks, or non‐embeddable blocks. These blocks are then sorted and grouped in terms of block type for block coding. Finally, a new intra‐block compression coding technique with small coded data for locating block elements is proposed to conduct effective compression and thereby reserve more space for embedding secret data. Experimental results indicate that the embedding rates of the BRBCC algorithm reach 3.9381 and 3.8436 bpp on the public datasets of BOSSbase and BOWS‐2, respectively, outperforming some state‐of‐the‐art RDHEI algorithms and exhibiting good application potential.

The advent of the fourth industrial revolution along with developments in other emerging technologies, such as Internet of Things, big data, artificial intelligence as well as cloud and quantum computing, smart healthcare systems (SHS) are becoming ubiquitous in our daily lives. Meanwhile, patients, doctors, and other medical personnel rely on the safe and efficient storage, transmission, and analysis of medical images and electronic health records for successful diagnosis, treatment, and management of different ailments. Moreover since, for various reasons, medical images are always the target of different illicit criminal activities, studies to utilise advanced information technologies to safeguard the confidentiality, integrity, and availability of such data have become a major priority in all SHS platforms. Our study evaluates recent efforts to deploy emerging technologies to design, secure, and enhance the efficiency of SHS that are based on medical images. It is hoped that this work will stimulate further interest aimed at the pursuit of more advanced algorithms and frameworks covering all aspects of security and privacy in emerging and future smart healthcare applications.

Loneliness and isolation are on the rise worldwide, threatening human well-being and the wellness of different age groups and backgrounds. Notably, global social distancing measures during the COVID-19 crisis have exacerbated this problem, resulting in various psychological and physiological ailments. Within both the categories of social and medical robots, companion robots are capable of engaging emotionally with users and providing continuous monitoring and assessment of their health. In this study, we propose a framework for modeling the emotion space of companion robots to facilitate their emotion generation and transition based on Plutchik’s wheel of emotions and reversible quantum circuit schemes. Superposition encodings allow fewer computing resources for the generation and storage of emotional states, and by using unitary operations, they facilitate easier emotion transition and recovery over different intervals. Further, an encryption strategy is designed based on the emotion communication architecture to secure the emotion-related data in human-robot interaction. It is hoped that such an integrative framework and research agenda exploring the role of companion robots will be useful to care for users’ social health by mitigating their negative emotions, especially during difficult times.