Image Encryption Algorithm Based on Convolutional Neural Networks and Dynamic S-Box Generation
For image encryption, this provides a more secure and adaptive approach by integrating CNNs with cryptography, though it is incremental over existing dynamic S-box methods.
The paper proposes an image encryption algorithm that uses CNNs to generate dynamic S-boxes, improving security against attacks. Security metrics (entropy, NPCR, UACI) show the scheme is more resilient than traditional methods.
The paper proposes a dynamic approach to image encryption, combining the use of Convolutional Neural Networks (CNNs) and classical cryptography to improve the security and flexibility of image encryption. The main concept is to create adaptive Substitution boxes (S-boxes) based on characteristics that are learned by a trained CNN. The CNN-based S-boxes can be relied on for more non-linearity, uniqueness, and input image dependence than the conventional fixed S-boxes because they are susceptible to the linear and differential attacks. This dynamic behaviour enhances the confusion property and makes it more resistant to statistical and structural attacks. The encryption algorithm consists of CNN-based feature extraction and the creation of a personalised S-box to replace the pixels. Entropy, histogram analysis, correlation, NPCR, and UACI enable security assessment of generated S-boxes based on the CNN, indicating that the scheme is more resilient and flexible than traditional ones.