Optimized AES with GAN Model for Secure Medical Image Transmission

Abstract

The rapid technological development and increased computational capabilities, cybersecurity risks are on the rise. This has led to a growing need for cutting-edge security algorithms, especially in fields like healthcare where medical images play a crucial role in diagnosing various conditions. As these images are frequently transmitted over the internet, safeguarding them from cyber threats is essential. The new framework for encryption is named PSO-AES-GAN(PSAGA). This paper introduces PSO based AES for encryption and generative GAN (Generative Adversarial Network) for key generation to strengthen the security of medical images. The model leverages an AES with PSO (Particle Swarm Optimization) encryption, SHA- 256 hash table, and GAN deep learning techniques. A SHA- 256 hash-table-based equation and AES with PSO enhance key entropy. Differential Huffman Compression (DHC) is utilized to compress encrypted images low-loss. The medical images have undergone testing using this model and assessed using performance metrics such as entropy, Encryption time, Decryption time, and Compared encryption algorithms such as chaotic maps, DES, AES, and Blowfish with similarity. Results show that the suggested model outperforms current methods.