Advanced image encryption algorithm integrating chaotic image encryption and convolutional neural networks

With the rapid growth of information technology, safeguarding the security of images has become a crucial area of study. This study introduces a method that combines chaotic image encryption with convolutional neural networks (CNNs) to enhance both security and efficiency. To create strong image encryption, the approach combines the sophisticated feature extraction capabilities of a CNN model with the randomness and nonlinear mapping of chaotic sequences. The basic principles of CNN and chaotic image encryption are outlined. A Convolutional Neural Network (CNN), a deep learning model characterized by weight sharing and a local perceptual field, effectively reconstructs high-level image features. Meanwhile, chaotic image encryption leverages nonlinear transformations and chaotic sequence generation are used to jumble pixel values, ensuring secure encryption. These procedures consist of feature extraction, pixel value mapping, key management, and chaotic sequence production. To accomplish high-strength encryption, CNN is used to extract high-level picture properties and perform difference actions among the chaotic patterns and image pixel values. Lastly, the approach is tested experimentally by contrasting it with more conventional chaotic picture encryption techniques. The experimental findings show that the picture encryption technique offers advantages in computational efficiency and the speed of encryption and decryption, along with significant enhancements in encryption quality and security.