Xianying Xu, Yidan Xu, Yinghong Cao, Herbert Ho-Ching Iu, Santo Banerjee, Nanrun Zhou, Junxin Chen
As the application of image emotion recognition technology grows increasingly widespread, emotional data faces potential privacy risks during transmission and storage. Images depicting negative emotions are particularly prone to revealing an individual is psychological state and sensitive information. Therefore, effective security protection for such images is of practical importance. This paper design a chaotic system and use CNN-based recognition to select the images requiring enhanced protection. First, a novel second-order memristor is designed and coupled with a neuron model to construct a chaotic system (SOM-Rulkov). Analysis of its phase diagram, bifurcation diagram, and Lyapunov exponent spectrum indicates that SOM-Rulkov exhibits rich dynamical characteristics, which can provide a pseudo-random key stream with good performance for encryption algorithms. Then, using a CNN to recognise emotions in images, employing the identified negative emotion images as encryption images to enhance data security during transmission and storage. During the encryption process, This paper proposes an enhanced diffusion structure with a parallel pool mechanism that enables four-directional diffusion to improve encryption efficiency. Experimental results show that the proposed scheme achieves strong security and high speed for emotional image privacy protection.