Multi-Size Image Encryption Algorithm Based on Fractional-Order Cellular Neural Network

Under the background of multi-channel and multi-network interwoven transmission, a large amount of information has been realized about long-distance transmission across the region and over time through Internet technology. However, more and more personal information is being violated and stolen in transit, which has made information owners increasingly concerned about whether the information is effectively secure during time out of their control. Therefore, it is necessary to design an encryption algorithm that meets people’s security standards. In this paper, a multi-size image encryption scheme based on an Fractional-Order Cellular Neural Network model is proposed. Firstly, DCT compression technology is applied to compress the transmitted image data to save encryption time. Secondly, DNA coding technology is applied to convert the image to a DNA image, and the scrambling process is realized by combining the improved Zigzag transform and spiral technology. In the diffusion stage, the pixel information is further hidden by DNA polyploid mutation technology, and the final ciphertext image is obtained by DNA decoding. The selection and scrambling of coding rules are applied to the generated chaotic sequence to ensure the randomness of the algorithm. Finally, through simulation verification and analysis of relevant test results, It can be proved that the encryption scheme in this paper can resist various external attacks.