A Secure Image Encryption Scheme based on Fully-Connected-Like Neural Network and Edge Pixel Reset

Abstract

When digital images are transmitted and stored in the currently open network environment, they often face various risks. A secure image encryption based on Fully-Connected-Like Neural Network (FCLNN) and edge pixel reset is proposed. Firstly, using random noise to reset the image last-bit of the edge pixels to generate different keys for each encryption. Secondly, the image rows and columns are transformed by Cyclic Shift Transformation (CST), and the moving step is determined according to the chaotic sequence. Then, the image is diffused at the bit-level by using FCLNN. Finally, forward and reverse diffusions are performed on the image to generate the cipher image. In addition, the result of convolution operation between plain image and chaotic sequence is introduced to set the initial value of the chaotic system to establish the correlation between plain image and algorithm, which makes the algorithm resistant to known/chosen plaintext attack. The simulation results show that the proposed algorithm has negligible loss, and the decrypted image is visually identical to the original image. At the same time, the algorithm has a large key space, can resist common attacks such as statistical attacks, differential attacks, noise attacks, and data loss attacks, and has high security.