Leveraging Convolutional Neural Networks for Enhanced Secure Encryption and Decryption

Özet

The need for safe data transfer is rising, and old cryptographic techniques are finding it harder to strike a balance between security, complexity, and speed. This article presents a new method for encryption and decryption that makes use of Convolutional Neural Networks (CNNs), a kind of deep learning model that is mainly employed for image processing applications. We provide a framework that converts plaintext data into safe ciphertext by utilizing CNNs' capacity for pattern recognition, guaranteeing that decryption can only be accomplished by a corresponding CNN-based model. Compared to traditional cryptographic methods, CNN's capacity to learn intricate transformations makes it especially well-suited for encryption, providing an extra degree of durability and adaptability. Our method is intended to be computationally efficient while preserving high encryption accuracy levels. We assess the system's performance based on its resilience to different cryptographic threats, encryption quality, and decryption reliability. Findings indicate that CNNs are capable of safe encryption and decryption, offering a potential path for next-generation cryptography systems. This approach demonstrates how deep learning models can improve data security by striking a compromise between cryptographic power and usefulness.