Hybrid Attention-GAN Framework for Secure Data Encryption and Decryption: Leveraging Transformer-based Attention Mechanisms and Adversarial Learning

This paper introduces a novel Hybrid Attention-GAN Framework that integrates transformer-based attention mechanisms with generative adversarial learning to perform secure data encryption and decryption. The proposed model employs a Transformer encoder to capture complex dependencies in plaintext and a GAN generator to produce highly obfuscated ciphertext that closely resembles random noise. On the decryption side, a self-supervised Transformer decoder reconstructs the original data without needing explicit plaintext-ciphertext pairs, enhancing both adaptability and security. The key contributions include: The first dual-phase encryption-decryption architecture combining Transformers and GANs. Use of self-supervised learning for decryption, significantly improving accuracy while reducing training overhead.