In medical information systems, image data can be considered crucial information. As imaging technology and methods for analyzing medical images advance, there will be a greater wealth of data available for study. Hence, protecting those images is essential. Image encryption methods are crucial in multimedia applications for ensuring the security and authenticity of digital images. Recently, the encryption of medical images has garnered significant attention from academics due to concerns about the safety of medical communication. Advanced approaches, such as e-health, smart health, and telemedicine applications, are employed in the medical profession. This has highlighted the issue that medical images are often produced and shared online, necessitating protection against unauthorized use.
Steganography is an embedded technique that hides the message in a digital medium for the purpose of safely transferring the message. The objective of this paper is to develop and improve lightweight hiding techniques used in the systems of smartphones, by improving the well-known Least Significant Bit (LSB) algorithm by changing the attribute that is related to its work and suggesting some additions in a way that does not affect processing speed or memory usage and increases security. It also provides a large area for hiding a large secrete message with very little impact on the cover relative to the size of the cover used. This paper proposes several additions and modifications to the algorithm, including: using Pseudo Random Number Generator (PRNG) to determine pixels locations in the cover. Coding process was also proposed for the purpose of increasing randomization and provide statistical distribution unrelated to the original message. The LSB hiding process enables the use of less important bits per pixel. In this case, a distribution of hidden bits within one pixel was suggested by different distributions in each hiding process, making data of repeated hiding useless for the attackers. Various measures (MSE, PSNR, BER, and SSIM) were adopted for the purpose of measuring the quality and efficiency of the proposed algorithm through several experiments including cover image sizes in different dimensions with a variety of data for secret messages, whose results were better than other values.
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