A Jigsaw Puzzle Solver-based Attack on Block-wise Image Encryption for Privacy-preserving DNNs

TL;DR

This paper introduces a novel attack method based on jigsaw puzzle solving to recover visual information from block-wise encrypted images used in privacy-preserving DNNs, demonstrating the scheme's vulnerability.

Contribution

It presents a new jigsaw puzzle solver-based attack that effectively restores images encrypted with block and pixel shuffling, exposing security weaknesses.

Findings

Encrypted images are mostly restored using the proposed attack.

The attack demonstrates the vulnerability of block-wise image encryption schemes.

Encryption schemes using block and pixel shuffling are less robust than previously thought.

Abstract

Privacy-preserving deep neural networks (DNNs) have been proposed for protecting data privacy in the cloud server. Although several encryption schemes for visually protection have been proposed for privacy-preserving DNNs, several attacks enable to restore visual information from encrypted images. On the other hand, it has been confirmed that the block-wise image encryption scheme which utilizes block and pixel shuffling is robust against several attacks. In this paper, we propose a jigsaw puzzle solver-based attack to restore visual information from encrypted images including block and pixel shuffling. In experiments, images encrypted by using the block-wise image encryption are mostly restored by using the proposed attack.