🤖 AI Summary
This work addresses the vulnerability of traditional image encryption schemes that rely on fixed S-boxes lacking input dependency, rendering them susceptible to linear and differential cryptanalysis. To overcome this limitation, the authors propose a novel hybrid approach integrating convolutional neural networks (CNNs) with classical cryptographic principles. Specifically, a pre-trained CNN extracts salient features from the input image to dynamically generate a personalized S-box for pixel substitution. This method represents the first implementation of content-adaptive S-box generation driven by the plaintext image itself, substantially enhancing the nonlinearity, uniqueness, and resilience of the encryption process against statistical and structural attacks. Experimental results demonstrate superior performance over conventional techniques across multiple security metrics, including information entropy, histogram uniformity, pixel correlation, NPCR, and UACI, thereby achieving both heightened security and greater flexibility.
📝 Abstract
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.