A deep learning signed medical image based on cryptographic techniques

Abstract

Innovative medical multimedia communications technology requirements have enhanced safety principles, allowing significant advancement in security standards. In hospitals and imaging centers, massive amounts of medical images have been created. To successfully access the medical databases and utilize those rich resources in assisting diagnosis and research, image processing enabled communication solutions are necessary. Our article presents a rigorous verified model by employing deep learning to enhance the cryptographic performance of biomedical images using hybrid chaotic Lorentz map diffusion and de-oxyribonucleic acid (DNA) confusion stages. It consists of two encryption/decryption techniques, the initial signal is verified using digital signature and two unique non-consecutive stages of chaotic diffusion with a single DNA scrambling stage in between. The encoded secret bit stream is generated and used to encrypt or decode the original signal in the diffusion manner to disintegrate the redundancy in the plain image statistics, utilizing hybrid chaotic system. Using DNA confusion step to make the relationship between the original signal and the utilized key more ambiguous. These stages make the proposed image cryptosystem more resistant to known/ chosen plaintext assaults. The performance of the suggested technique will be assessed to the most similar techniques reported in the literature for comparative purposes.

Enhanced ARIA-based counter mode deterministic random bit generator random number generator implemented in verilog

“…Secondly, although the reseeding and initialization functions in CTR-DRBG share nearly identical structures, separate implementations are required due to different input values. This redundancy induces unnecessary duplicate structures, potentially increasing chip size in hardware implementations [42]. Thirdly, while CTR-DRBG generates a key stream to provide randomness, the use of the counter value increases predictability in the key stream [43].…”