Hyperchaotic Image Encryption System Based on Deep Learning LSTM
Shuangyuan Li, Mengfan Li, Qichang Li, Yanchang Lv · International Journal of Advanced Computer Science and Applications · 2023
This paper introduces an advanced method for enhancing the security of image transmission. It presents a novel color image encryption algorithm that combines hyperchaotic dynamics and deep learning medium and long short-term memory (LSTM) networks. Firstly, the chaotic sequence is generated using the Lorenz hyperchaotic system, then the Lorenz chaotic system is discretized and iteratively processed using the fourth-order Runge-Kutta (RK4) method, and then the deep learning LSTM model is used to transform the chaotic sequence processed by the Lorenz hyperchaotic system into a new sequence for training. Finally, according to the new chaotic signal, the Arnold disruption and Deoxyribo Nucleic Acid (DNA) encoding double disruption diffusion are performed to derive the ultimate encrypted image. Through the analysis of multiple color image simulation experiments, the algorithm presented in this paper can well realize the encryption on color images and can achieve lossless encryption, with strong resistance to differential attack, statistical attack and violent attack. Compared with the literature analysis, the correlation coefficient, information entropy and pixel change rate of this paper are closer to the ideal value, and it has higher security and better encryption effect.