Optimization of Image Encryption Algorithm Based on Henon Mapping and Arnold Transformation of Chaotic Systems

This study introduces an optimized image encryption algorithm that integrates Henon mapping and Arnold transformation to enhance the security and randomness of digital image encryption. The algorithm is designed to address the vulnerabilities of open network environments where data theft or corruption can compromise image quality during encryption. Initially, images are processed through grayscale conversion to reduce dimensionality, followed by Henon mapping to induce a chaotic sequence that scrambles the pixel matrix. Subsequently, Arnold transformation is applied, iterating 100 times to further disrupt the image structure, ensuring a high level of diffusion and complexity. The proposed method demonstrates superior performance with an average pixel change rate (NPCR) of 0.9982 and a normalized average change intensity (NACI) of 0.3654, significantly increasing resistance to differential attacks. The encrypted images exhibit higher information entropy and effectively mask the original data, although at the cost of extended encryption time due to the dual scrambling process. The study concludes that the combined use of Henon mapping and Arnold transformation not only strengthens encryption against various attacks but also introduces a novel approach to image encryption, with potential for further optimization to enhance efficiency in handling larger images. This advancement is crucial for protecting privacy and ensuring data integrity in the transmission and storage of images, particularly in the face of evolving cyber threats.