Chua's diode and strange attractor: a three‐layer hardware–software co‐design for medical image confidentiality. Issue 7 (23rd April 2020)

• Record Type: Journal Article
• Authors: Rajagopalan, Sundararaman; Poori, Siva; Narasimhan, Mukund; Rethinam, Sivaraman; Vallipalayam Kuppusamy, Chandrasekar; Balasubramanian, Ramalingam; Moorthi Paramasivam Annamalai, Vijaya; Rengarajan, Amirtharajan
• Is Part Of: IET image processing. Volume 14:Issue 7(2020)
• Page Start: 1354
• Page End: 1365
• Publication Date: 2020-04-23
• Subjects: random number generation -- cellular automata -- chaos -- security of data -- cryptography -- medical image processing -- image processing; Chua's diode -- strange attractor -- three‐layer hardware–software co‐design -- medical image confidentiality -- medical image security -- improved techniques -- diffusion processes -- pixel level manipulations -- nonlinear circuits -- hardware components -- medical image encryption -- Chua diode circuit -- 256 × 256 random synthetic image -- DICOM image -- scrambling diffusion -- average correlation coefficients -- encrypted pixels; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0562
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Medical image security relies upon the arrival of improved techniques for enhancing the confusion as well as diffusion processes in the pixel level manipulations. Non‐linear circuits constructed with hardware components can be employed for hardware–software co‐design in medical image encryption. The proposed approach has adopted Chua diode circuit for the generation of 256 × 256 random synthetic image with the entropy of 7.9972. This synthetic image has been utilised to scramble the DICOM image for the second level of diffusion as the cellular automata provides the first level of scrambling and diffusion. The average correlation coefficients of encrypted pixels are 0.00204, −0.00298 and −0.00054 in three directions. The encrypted pixels pass the NIST SP 800‐22 test suite and offer resistance to statistical, differential and chosen plain text attacks.