An image encryption algorithm based on DNA sequence operations and cellular neural network

作者：Norouzi Benyamin^*; Mirzakuchaki Sattar

来源：Multimedia Tools and Applications, 2017, 76(11): 13681-13701.

DOI：10.1007/s11042-016-3769-4

• Image encryption
• DNA operations
• Cellular neural network
• Bit-substitution

This paper presents a new way of image encryption based on biologic DNA sequence operations and Cellular Neural Network (CNN), which consists of three processes; bit-substitution, key stream generation process, and diffusion process. Firstly, a plain-image is equally divided into four sub-images and a DNA sequence matrix of each sub-image is obtained. Then we employed the hamming distance (between DNA sequences) and DNA sequence operation to encrypt each DNA sub-image. The second process is a pseudo-random key stream generator based on Cellular Neural Network. The parameters and initial conditions of the CNN system are derived using a 256 bit-long external secret key by applying some algebraic transformations to the key. The original key stream is related to the plain-image which increases the level of security and key sensitivity of the proposed algorithm. In the final process, we use the chaotic sequences generated by CNN to modify the pixel gray level values and crack the strong correlations between adjacent pixels of an image simultaneously. This feature will significantly increase plaintext sensitivity. Moreover, in order to reach higher security and higher complexity, the proposed method employs the image size in key stream generation process. The experimental results reveal that the new image encryption algorithm has the advantages of large key space (2(256)), high security, high sensitivity (Number of Pixels Change Rate: NPCR > 99.6201 %, Unified Average Changing Intensity: UACI > 33.5065 %), and high entropy (> 7.9975). Also, the distribution of gray level values of the encrypted image has a semi-random behavior.

• 出版日期2017-6