Block Scrambling Image Encryption Used in Combination with Data Augmentation for Privacy-Preserving DNNs
Tatsuya Chuman, Hitoshi Kiya

TL;DR
This paper introduces a learnable image encryption method combining block scrambling and data augmentation to enhance privacy in DNNs while maintaining high classification accuracy.
Contribution
It presents a novel encryption approach that improves robustness against attacks and integrates data augmentation for privacy-preserving deep learning.
Findings
Effective in maintaining classification accuracy with encrypted images
Enhances robustness against various attacks
Demonstrated success in image classification experiments
Abstract
In this paper, we propose a novel learnable image encryption method for privacy-preserving deep neural networks (DNNs). The proposed method is carried out on the basis of block scrambling used in combination with data augmentation techniques such as random cropping, horizontal flip and grid mask. The use of block scrambling enhances robustness against various attacks, and in contrast, the combination with data augmentation enables us to maintain a high classification accuracy even when using encrypted images. In an image classification experiment, the proposed method is demonstrated to be effective in privacy-preserving DNNs.
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