Secure Reversible Transformations through Neural Coupling Architecture

• Author(s): Kunal Sood; Raunak Singh; Girish Mishra
• Paper ID: 1715290
• Page: 1621-1633
• Published Date: 20-03-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 9 March-2026

Abstract

This work presents a neural-based cryptographic framework that integrates properties of both block ciphers and stream ciphers through an invertible coupling network. The model employs afixed-key, 128-bit transformation in which encryption and decryption are learned jointly, while an adversarial network attempts unauthorized plaintext recovery. Using Real-NVP-style affine coupling layers, the system ensures exact invertibility and secure reversible transformations. Adversarial training enables near-perfect reconstruction for the legitimate receiver while maintaining high uncertainty for the adversary. By combining fixed transformations with continuous ciphertext outputs and noise-based perturbations, the framework exhibits dual characteristics of classical block and stream ciphers. Experimental results demonstrate the effectiveness of the approach as a hybrid neural cryptographic mechanism, providing secure, learnable encryption without hand-designed structures.

Keywords

Adversarial Learning, Hybrid Block–Stream Cipher, Invertible Neural Networks, Key-Conditioned Encryption, Neural Cryptography

Citations

IRE Journals:
Kunal Sood, Raunak Singh, Girish Mishra "Secure Reversible Transformations through Neural Coupling Architecture" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 1621-1633 https://doi.org/10.64388/IREV9I9-1715290

IEEE:
Kunal Sood, Raunak Singh, Girish Mishra "Secure Reversible Transformations through Neural Coupling Architecture" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1715290