Security Evaluation of Compressible and Learnable Image Encryption against Jigsaw Puzzle Solver Attacks
Year: 2023
Type: conference-paper
Abstract: Several learnable image encryption schemes have been developed for privacy-preserving image classification. This paper focuses on the security of 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.
Authors: Tatsuya Chuman, Nobutaka Ono, Hitoshi Kiya
Institution: Tokyo Metropolitan University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4388720163