Exploiting compressed sensing and polynomial-based progressive secret image sharing for visually secure image selection encryption with authentication

Abstract

Image encryption is an effective method to protect private images by converting them into meaningless ones, but many current image encryption algorithms still have security and efficiency problems. This paper combines compressed sensing (CS) and secret image sharing (SIS) to propose a certifiable visually secure image selection encryption. Firstly, a selective encryption based on multi-task convolutional neural network (SE-MTCNN) is presented to distinguish and encrypt the sensitive and non-sensitive information of plain images. Subsequently, the SIS algorithm is used to decompose the resulting plain image into multiple shadow images to disperse the risk of cryptographic image loss and resist hacking attacks. Herein, a pseudo-convolutional sliding scrambling based on chaotic sequences driven by plaintext (PCSS-CSDP) is proposed to eliminate the correlation between adjacent image pixels. Finally, the IWT-M-embedding algorithm is used to embed shadow images into carrier images to get multiple visually meaningful cipher images. Additionally, a new authentication scheme based on bit-level cyclic shifts and dynamic combination (AS-BCSDC) is introduced, and the obtained authentication information is fused into cipher images to prevent the deception of illegal users and malicious tampering of cloud platform. Experiments demonstrate the effectiveness of our algorithm, and it may be applied for image secure communication.