Medical image characteristic region recognition encryption algorithm based on intra and inter blocks scrambling and LSCC chaotic map

Chen, Rong; Zhang, Fan; Teng, Lin; Wang, Xingyuan

doi:10.1007/s11042-023-15458-y

Cited by 1 publication

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chen et al first split an image into different sub-blocks and then those blocks are scrambled using the designed intra and inter scrambling methods. In addition, Fisher-Yates shuffling is utilized to further improve the scrambling effect [19]. Wang et al used three different scrambling methods to scramble a color image.…”

Section: Introductionmentioning

confidence: 99%

Image encryption algorithm based on improved iterative chaotic map with infinite collapses and Gray code

Wang,

Zhang,

Zhao

2024

Phys. Scr.

View full text Add to dashboard Cite

The quick advancement of multimedia technology has led to the widespread utilization of digital images across various industries. Meanwhile, image security has become an urgent issue in need of resolution. To guarantee image security as well fulfill the requirements of real-time image cryptosystems, a novel image encryption algorithm is proposed based on the designed improved iterative chaotic map with infinite collapses (ICMIC), Gray code and semi-pixel level permutation and diffusion. Firstly, two chaotic sequences are produced using the improved ICMIC and then they are used to perform scrambling and diffusion operations. Secondly, the plain image is encoded using Gray code and then we convert each Gray code into two semi-pixels. Thirdly, the designed semipixellevel permutation is used to scramble the semi-pixel vector. Finally, the scrambled semi-pixel vector is diffused using the designed dynamic semi-pixel level diffusion. The performance analyses illustrate that the proposed algorithm possesses robust security and high efficiency, making it ideally suited for application in real-time image cryptosystems.

show abstract

Section: Introductionmentioning

confidence: 99%