Cryptanalysis of Dual-Stage Permutation Encryption Using Large-Kernel Convolutional Neural Network and Known Plaintext Attack

Abstract

Reversible data-hiding in encrypted images (RDHEI) plays a pivotal role in preserving privacy within images stored on cloud platforms. Recently, Wang et al. introduced a dual-stage permutation encryption scheme, which is highly compatible with RDHEI techniques. In this study, we undertake an exhaustive examination of the characteristics inherent to the dual-stage permutation scheme and propose two cryptanalysis schemes leveraging a large-kernel convolutional neural network (LKCNN) and a known plaintext attack (KPA) scheme, respectively. Our experimental findings demonstrate the effectiveness of our cryptanalysis schemes in breaking the dual-stage permutation encryption scheme. Based on our investigation, we highlight significant security vulnerabilities in the dual-stage permutation encryption scheme, raising concerns about its suitability for secure image storage and privacy protection in cloud environments.