Remote Sensing Image Encryption Using Optimal Key Generation-Based Chaotic Encryption

The Internet of Things (IoT) offers a new era of connectivity, which goes beyond laptops and smart connected devices for connected vehicles, smart homes, smart cities, and connected healthcare. The massive quantity of data gathered from numerous IoT devices poses security and privacy concerns for users.With the increasing use of multimedia in communications, the content security of remote-sensing images attracted much attention in academia and industry. Image encryption is important for securing remote sensing images in the IoT environment. Recently, researchers have introduced plenty of algorithms for encrypting images. This study introduces an Improved Sine Cosine Algorithm with Chaotic Encryption based Remote Sensing Image Encryption (ISCACE-RSI) technique in IoT Environment. The proposed model follows a three-stage process, namely pre-processing, encryption, and optimal key generation. The remote sensing images were preprocessed at the initial stage to enhance the image quality. Next, the ISCACERSI technique exploits the double-layer remote sensing image encryption (DLRSIE) algorithm for encrypting the images. The DLRSIE methodology incorporates the design of Chaotic Maps and deoxyribonucleic acid (DNA) Strand Displacement (DNASD) approach. The chaotic map is employed for generating pseudorandom sequences and implementing routine scrambling and diffusion processes on the plaintext images. Then, the study presents three DNASD-related encryption rules based on the variety of DNASD, and those rules are applied for encrypting the images at the DNA sequence level. For an optimal key generation of the DLRSIE technique, the ISCA is applied with an objective function of the maximization of peak signal to noise ratio (PSNR). To examine the performance of the ISCACE-RSI model, a detailed set of simulations were conducted. The comparative study reported the better performance of the ISCACE-RSI model over other existing approaches. © 2023 CRL Publishing. All rights reserved.