Temper wolf hunt optimization enabled GAN for robust image encryption

Abstract

In today’s digital era, the security of sensitive data, particularly in the realm of multimedia, is of paramount importance. Image encryption serves as a vital shield against unauthorized access and ensures the confidentiality and integrity of visual information. As such, the continuous pursuit of robust and efficient encryption techniques remains a pressing concern. This research introduces a Temper Wolf Hunt Optimization enabled Generative Adversarial Network Encryption model (TWHO-GAN), designed to address the challenges of image encryption in the modern digital landscape. TWHO, inspired by the collective hunting behavior of wolf and coyote packs, is employed to generate highly secure encryption keys. This algorithm excels in exploring complex solution spaces, creating robust, attack-resistant keys. In TWHO-GAN model, GANs are employed to create encrypted images that are virtually indistinguishable from their original counterparts, adding a layer of security by generating complex encryption keys and ensuring robust protection against attacks. The GAN component reconstructs the encrypted images to their original form when decrypted with the correct keys, ensuring data integrity while maintaining confidentiality. Further, the significance of the proposed model relies on the TWHO algorithm formulated by the integration of the adaptability and coordinated hunting strategies to optimize the chaotic map generation in image encryption protecting the sensitive visual information from unauthorized access as well as potential threats. Through extensive experimentation and comparative analysis, TWHO-GAN demonstrates superior performance in image encryption, surpassing former methods in terms of Cs, 𝐻𝑖𝑠C, MSE, PSNR, RMSE, and SSIM attaining values of 0.93, 94.19, 3.274, 59.70 dB, 1.8095, and 0.940 respectively for 5 numbers of images. Moreover, the TWHO-GAN approach attained the values of 0.91,92.22, 2.03, 49.74 dB, 1.42, and 0.88 for Cs, HisC, MSE, PSNR, RMSE, and SSIM respectively utilizing the Airplanes dataset. The model exhibits robust resistance to various attacks, making it a compelling choice for secure image transmission and storage.