A novel image encryption/decryption scheme based on chaotic neural networks Nooshin Bigdeli, Yousef Farid n , Karim Afshar EE Department, Imam Khomeini International University, Daneshgah Blv., Qazvin, Iran article info Article history: Received 9 March 2011 Received in revised form 10 November 2011 Accepted 6 January 2012 Available online 9 February 2012 Keywords: Secure communication Cipher-image Chaotic neuron layer (CNL) Permutation neuron layer (PNL) Tent map abstract This paper presents a novel image encryption/decryption algorithm based on chaotic neural network (CNN). The employed CNN is comprised of two 3-neuron layers called chaotic neuron layer (CNL) and permutation neuron layer (PNL). The values of three RGB (Red, Green and Blue) color components of image constitute inputs of the CNN and three encoded streams are the network outputs. CNL is a chaotic layer where, three well-known chaotic systems i.e. Chua, Lorenz and L ¨ u systems participate in generating weights and biases matrices of this layer corresponding to each pixel RGB features. Besides, a chaotic tent map is employed as the activation function of this layer, and makes the relationship between the plain image and cipher image nonlinear. The output of CNL, i.e. the diffused information, is the input of PNL, where three-dimensional permutation is applied to the diffused information. The overall process is repeated several times to make the encryption process more robust and complex. A 160-bit-long authentication code has been used to generate the initial conditions and the parameters of the CNL and PNL. Some security analysis are given to demonstrate that the key space of the new algorithm is large enough to make brute-force attacks infeasible and simulations have been carried out with detailed numerical analysis, demonstrating the high security of the new image encryption scheme. & 2012 Elsevier Ltd. All rights reserved. 1. Introduction In the recent years, secure private communication methods have aroused the interest of many researchers all over the world. The most general architecture for image encryption is the permutation–diffusion architecture. There are two iterative stages in this kind of image cryptosystems (Chen et al., 2004). The permutation stage changes the position of image pixels but does not alter their values. In the diffusion stage, the pixel values are modified sequentially so that a tiny change in one pixel is spread out to almost all pixels in the whole image. The whole permutation–diffusion round repeats for a number of times so as to achieve a satisfactory level of security. For this architecture, generation of secret keys and control parameter are essential issues in increasing security and complexity of the algorithm. A good encryption algorithm should be sensitive to the cipher keys, and the key space should be large enough to make brute- force attacks infeasible. In order to achieve such type of security, employing chaotic systems in generating the secret keys and parameters has become one of the most important topics in secure communications (Lian, 2009). In the literature, lots of encryption methods are proposed which are based on using chaotic systems in this era (Wei et al., 2006; Joshi et al., 2009; Tong and Cui, 2008; Tong and Cui, 2009; Wong et al., 2008). For the special properties such as parameters and initial-value sensi- tivity, ergodicity and quasi-randomness, chaos is used in data protection, widely (Lian, 2009). Due to their good properties such as high nonlinearity, para- meter sensitivity and learning ability, neural networks have been widely used as the other choice for information protection, such as data encryption, data authentication and intrusion detection (Lian, 2009; Chan and Cheng, 2001; Xiao et al., 2005). Neural networks’ confusion and diffusion properties have been used to design encryption algorithms, such as the stream ciphers (Chan and Cheng, 2001; Karras and Zorkadis, 2003) or the block ciphers (Lain et al., 2004; Lian, 2009). As a combination of neural networks and chaos, a chaotic neural network (CNN), has both the characteristic of neural network and chaos. Especially it has more complex dynamic behavior and so, it is expected to have better performance as an image encryption tool. Therefore, such combinations have been employed in some researches as more efficient methods for information protection and information encryption (Lian, 2009). As an instance, in (Lian et al., 2006) a three-layer neural network has been used to construct a hash function. The three Contents lists available at SciVerse ScienceDirect journal homepage: www.elsevier.com/locate/engappai Engineering Applications of Artificial Intelligence 0952-1976/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.engappai.2012.01.007 n Corresponding author. Tel./fax: þ98 281 8371155. E-mail addresses: bigdeli@ikiu.ac.ir (N. Bigdeli), yousef.farid@ikiu.ac.ir, y.farid.e.control@gmail.com (Y. Farid), afshar@ikiu.ac.ir (K. Afshar). Engineering Applications of Artificial Intelligence 25 (2012) 753–765