ArticleApplied biochemistry and biotechnology2023
Time Efficient Image Encryption-Decryption for Visible and COVID-19 X-ray Images Using Modified Chaos-Based Logistic Map.
Article in Applied biochemistry and biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 20 citations in OpenAlex.
- Integrative framework for cancer detection via integro-differential equations using deep learning techniques.Scientific reports · 2026Article
- Deep reinforcement learning-based reversible medical image encryption framework for secure IoMT environments.Frontiers in artificial intelligence · 2026Article
- Medical Image Encryption Using Chaotic Mechanisms: A Study.Bioengineering (Basel, Switzerland) · 2025Review
- Enhanced security for medical images using a new 5D hyper chaotic map and deep learning based segmentation.Scientific reports · 2025Article
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Authors and funding
3 authors at 2 institutions in 1 country.
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Abstract
In this pandemic situation, radiological images are the biggest source of information in healthcare and, at the same time, one of the foremost troublesome sources to analyze. Clinicians now-a-days must depend to a great extent on therapeutic image investigation performed by exhausted radiologists and some of the time analyzed and filtered themselves. Due to an overflow of patients, transmission of these medical data becomes frequent and maintaining confidentiality turns out to be one of the most important aspects of security along with integrity and availability. Chaos-based cryptography has proven a useful technique in the process of medical image encryption. The specialty of using chaotic maps in image security is its capability to increase the unpredictability and this causes the encryption robust. There are large number of literature available with chaotic map; however, most of these are not useful in low-precision devices due to their time-consuming nature. Taking into consideration of all these facts, a modified encryption technique is proposed for 2D COVID-19 images without compromising security. The novelty of the encryption procedure lies in the proposed design which is split into mainly three parts. In the first part, a variable length gray level code is used to generate the secret key to confuse the intruder and subsequently it is used as the initial parameter of both the chaotic maps. In the second part, one-stage image pixels are shuffled using the address code obtained from the sorting transformation of the first logistic map. In the final stage, a complete diffusion is applied for the whole image using the second chaotic map to counter differential and statistical attack. Algorithm validation is done by experimentation with visual image and COVID-19 X-ray images. In addition, a quantitative analysis is carried out to ensure a negligible data loss between the original and the decrypted image. The strength of the proposed method is tested by calculating the various security parameters like correlation coefficient, NPCR, UACI, and key sensitivity. Comparison analysis shows the effectiveness for the proposed method. Implementation statistics shows time efficiency and proves more security with better unpredictability.
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