Medical records are extremely sensitive information and may cause a violation of patients' rights if the data is captured or altered. Such data requires uncompromising security during transmission or when the data is stored in medical datacenters. The most commonly used procedure is to hide sensitive information inside a cover medium without incurring any increase in data size or computational overhead. Even though many steganography techniques have been proposed in recent years, only a few have addressed issues related to protecting medical data. In this paper, we proposed a data hiding method based on magic cube generated using magic matrix. In the proposed method, 9 bits of secret data were embedded in each group of 4 pixels using secret keys that scattered values in the square template to make it more unpredictable. Experimental results showed that the proposed method achieved an embedding capacity of 2.25 bpp and an average PSNR of 44 dB outperforming previous methods. INDEX TERMS Steganography, magic cube, irreversible information hiding, magic matrix, embedding capacity.

Recently, sensor networks have emerged as a high-impact research area, and a number of high profile applications have been proposed. Although significant progress has already been made on securing basic network protocols, additional research is needed to produce techniques and methods for protecting canonical tasks in wireless sensor networks. In this paper, we propose an effective self-embedding authentication watermarking method for tampered location detection and image recovery. The proposed detection method is classified into block-wise and pixel-wise. In block-wise detection, if the size of the block is small, the false positive rate (FPR) will be low. In pixel-wise detection, when the tampered pixels are detected, only the corresponding pixel area is marked. Therefore, the FPR will be lower than that of the block-wise detection. The experimental results demonstrate that the proposed method was effective, and accurate tamper detection and high-quality recovery can be realized even in highly tampered images.

The current pandemic has modified how education, learning, and technology interact with one another inside universities. The usage of technology for instructional purposes raises the question of whether learning that happens in an online environment is as effective as traditional classroom models. Within this context, this study explores the psychological well-being of students during the COVID-19 pandemic, using an online cross-sectional survey. Data were collected from 246 university students currently studying at a private university in India. Hierarchical regression analysis and structural equation modelling were used to study the mediating effects between communication apprehension, perceived learning, and psychological well-being under the moderating effects of intention to use social media and psychological stress. Results show that higher intentions to use social media alleviated the negative effects of communication apprehension on perceived learning. Interestingly, it was also found that perceived learning had a significant positive relationship with psychological well-being when students experienced higher levels of psychological stress (eustress). Based on the technology acceptance model (TAM) and the transactional theory of stress and coping, we attempt to integrate the findings related to these theories, which can be considered distinct to previous studies. Implications, limitations, and future directions for research and practice have also been discussed.

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