Image encryption based on adversarial neural cryptography and SHA controlled chaos

Abstract

To overcome the vulnerability to known-plaintext attack or chosen-plaintext attack of a linear image encryption system, a new approach for image encryption is proposed based on adversarial neural cryptography (ANC) combined with SHA-256 controlled chaotic systems. In this image encryption approach, the optimal network model is first obtained by training a generative adversarial network (GAN), and then the GAN model is used to achieve a noise-like intermediate image. Subsequently, the XOR operation based on a logistic map is performed on the intermediate image to obtain the final ciphertext. The intrinsic non-linearity of the neural network (NN) guarantees the ability of the proposed system to resist common attacks like known-plaintext attack or chosen-plaintext attack. The plaintext dependent SHA-256 controlled logistic map greatly improves the diffusion performance so that the encryption system can resist differential attacks. Numerical simulation results prove the reliability, effectiveness, and security of the proposed scheme.