Hybrid chaotic system-oriented artificial fish swarm neural network for image encryption

According to the research works of the references on chaotic cryptanalysis, many recent chaotic image encryption algorithms cannot resist chosen-plaintext attacks. Although some chaotic image encryption algorithms introduce plain-image information, they still violate some design requirements of modern cryptosystems. The traditional image encryption algorithm has the disadvantages of low security and time-consuming. Therefore, after analyzing many literatures related to chaotic image encryption and aiming to improve the security of ciphertext and effectively resist plaintext attack, we propose a hybrid chaotic system-oriented artificial fish swarm neural network method for image encryption. Firstly, based on the nonlinear combination theory, the Logistic, Tent and Sine mappings are used to design the hybrid complex chaotic system. According to the pixel value of plaintext, it generates the initial value to output the chaotic sequence. Taking a chaotic sequence as the input layer, the artificial neural network is introduced to train and learn the chaotic sequence. It eliminates the chaotic periodicity and outputs the neural network sequence. The artificial fish swarm algorithm is used to optimize the initial weight and threshold of the Elman neural network. The set obfuscation method is defined to scramble the plaintext. A quantization method is constructed to quantify the neural network sequence and obtain the key flow. The mixed images are classified, the key flow is combined, the pixel value is changed and the ciphertext is output by designing the piecewise diffusion technology. Experimental results show that compared with the current state-of-the-art image encryption methods, the proposed algorithm has higher security and resist plaintext attack ability.