A Methodology Survey of Neural Cryptography using Convolutional Neural Network, Quantization and Key Exchange

The proposed study explores the use of convolutional neural networks (CNNs) in the field of neural cryptography, aiming to improve secure communication protocols with more adaptive, data-driven models. Moving away from traditional cryptographic methods, which rely on fixed mathematical algorithms, this work investigates how CNN-based systems can enhance encryption, decryption, authentication and resistance to unauthorized access. The methodology follows a systematic and iterative process, involving repeated cycles of training, evaluation, and optimization to gradually improve the CNN models' performance in maintaining confidentiality, integrity and authentication across communication channels. Through extensive experimentation, it was found that how well CNN-based cryptographic systems tackle key security challenges such as encryption accuracy, decryption dependability and robust authentication. The study highlights the importance of ongoing evaluation and refinement to strengthen the effectiveness of neural cryptography frameworks. Ultimately, this exploration seeks to pave the way for more resilient and adaptable security measures, shaping the future of secure information exchange in our increasingly interconnected digital landscape.