A GAN-Based Image Transformation Scheme for Privacy-Preserving Deep Neural Networks

TL;DR

This paper introduces a GAN-based image transformation method that protects visual information in images and enhances robustness against attacks, enabling privacy-preserving image classification without encryption keys.

Contribution

The paper presents a novel GAN-based transformation scheme that preserves privacy and improves robustness against ciphertext-only attacks for deep neural network applications.

Findings

Effective protection of visual information in images.

Improved robustness against ciphertext-only attacks.

Maintains classification accuracy with protected images.

Abstract

We propose a novel image transformation scheme using generative adversarial networks (GANs) for privacy-preserving deep neural networks (DNNs). The proposed scheme enables us not only to apply images without visual information to DNNs, but also to enhance robustness against ciphertext-only attacks (COAs) including DNN-based attacks. In this paper, the proposed transformation scheme is demonstrated to be able to protect visual information on plain images, and the visually-protected images are directly applied to DNNs for privacy-preserving image classification. Since the proposed scheme utilizes GANs, there is no need to manage encryption keys. In an image classification experiment, we evaluate the effectiveness of the proposed scheme in terms of classification accuracy and robustness against COAs.