Security Evaluation of Compressible and Learnable Image Encryption Against Jigsaw Puzzle Solver Attacks
Abstract
Several learnable image encryption schemes have been developed for privacy-preserving image classification. This paper focuses on the security block-based image encryption methods that are learnable and JPEG-friendly. Permuting divided blocks in an image is known to enhance robustness against ciphertext-only attacks (COAs), but recently jigsaw puzzle solver attacks have been demonstrated to be able to restore visual information on the encrypted images. In contrast, it has never been confirmed whether encrypted images including noise caused by JPEG-compression are robust. Accordingly, the aim of this paper is to evaluate the security of compressible and learnable encrypted images against jigsaw puzzle solver attacks. In experiments, the security evaluation was carried out on the CIFAR-10 and STL-10 datasets under JPEG-compression.
Keywords
Cite
@article{arxiv.2308.02227,
title = {Security Evaluation of Compressible and Learnable Image Encryption Against Jigsaw Puzzle Solver Attacks},
author = {Tatsuya Chuman and Nobutaka Ono and Hitoshi Kiya},
journal= {arXiv preprint arXiv:2308.02227},
year = {2023}
}
Comments
To be appeared in 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE 2023)