GAN-Based Adversarial Encryption for Autonomous AI-Learned Cryptography

Praveen Kumar Reddy Idamakanti

doi:10.71097/IJSAT.v16.i3.7335

GAN-Based Adversarial Encryption for Autonomous AI-Learned Cryptography

Author(s)	Mr. Praveen Kumar Reddy Idamakanti
Country	India
Abstract	GAN-based adversarial encryption leverages Generative Adversarial Networks (GANs) to enable AI agents, typically named Alice (encryptor), Bob (decryptor), and Eve (eavesdropper), to learn encryption and decryption through an adversarial game. This approach allows for the autonomous development of cryptographic protocols without explicit programming of algorithms. Advancements include integrating Genetic Algorithms (GAs) with GANs (GA-GAN) to evolve more robust and complex encryption schemes, achieving properties like perfect secrecy (One-Time Pad) under strong adversarial conditions, and extending these principles to asymmetric key encryption. The GA-GAN approach, through co-evolution of generator and discriminator networks, shows promise for developing quantum-resistant cryptography by creating dynamic, non-static encryption methods.
Keywords	GAN-based encryption, adversarial encryption, AI cryptography, Alice Bob Eve model, encryption GANs, GA-GAN, genetic algorithm cryptography, deep learning encryption, neural encryption, perfect secrecy, one-time pad, quantum-resistant cryptography, dynamic encryption, co-evolution, asymmetric encryption, adversarial networks, secure communication, machine learning security, GAN cryptosystems, autonomous encryption.
Field	Computer > Network / Security
Published In	Volume 16, Issue 3, July-September 2025
Published On	2025-07-30
DOI	https://doi.org/10.71097/IJSAT.v16.i3.7335
Short DOI	https://doi.org/g9vzfq

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GAN-Based Adversarial Encryption for Autonomous AI-Learned Cryptography

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