Tuna Swarm Optimization with 3D-chaotic map and DNA encoding for image encryption with lossless image compression based on FPGA

Abstract

Images and video-based multimedia data are growing rapidly due to communication network technology. During image compression and transmission, images are inevitably corrupted by noise due to the influence of the environment, transmission channels, and other factors, resulting in the damage and degradation of digital images. Numerous real-time applications, such as digital photography, traffic monitoring, obstacle detection, surveillance applications, automated character recognition, etc are affected by this information loss. Therefore, the efficient and safe transmission of data has become a vital study area. In this research, an image compression–encryption system is proposed to achieve security with low bandwidth and image de-noising issues during image transmission. The Chevrolet transformation is proposed to improve image compression quality, reduce storage space, and enhance de- noising. A 3D chaotic logistic map with DNA encoding and Tuna Swarm Optimization is employed for innovative image encryption. This optimization approach may significantly increase the image's encryption speed and transmission security. The proposed system is built using the Xilinx system generator tool on a field-programmable gate array (FPGA). Experimental analysis and experimental findings show the reliability and scalability of the image compression and encryption technique designed. For different images, the security analysis is performed using several metrics and attains 32.33 dB PSNR, 0.98 SSIM, and 7.99721 information entropy. According to the simulation results, the implemented work is more secure and reduces image redundancy more than existing methods.

Local bit-level image encryption algorithm based on one dimensional zero excluded chaotic map

“…To enhance the security and efficiency of chaos-based image encryption algorithms, researchers have put forth various methodologies [17,18]. This has led to the integration of chaotic image encryption with diverse technologies, including matrix semi-tensor products [19,20], Boolean networks [21,22], neural networks [23][24][25], DNA encoding [26][27][28], elliptic curves [29,30], and quantum encryption 2. Designed chaotic system 2.1.…”

Chaotic image encryption algorithm with improved bonobo optimizer and DNA coding for enhanced security

“…When compared to other classical encryption algorithms, the experimental outcome shows that image encryption depends on the concept of chaos and has better features [ 4 ]. In many aspects, the study of Chaos has made tremendous progress [ 5 ]. Chaos meets the requirement of image encryption due to its features of chaos, like inherent randomness, and maximum sensitivity to primary conditions and control parameters in recent times, chaos research has become increasingly popular [ 6 ].…”