We propose a novel image transformation network for generating visually protected images
for privacy-preserving deep neural networks (DNNs). The proposed transformation network …

We propose a novel image transformation scheme using generative adversarial networks
(GANs) for privacy-preserving deep neural networks (DNNs). The proposed scheme …

We present a novel privacy-preserving scheme for deep neural networks (DNNs) that
enables us not to only apply images without visual information to DNNs for both training and …

We present a novel privacy-preserving scheme for deep neural networks (DNNs) that
enables us not to only apply images without visual information to DNNs but to also consider …

In this article, we propose a privacy-preserving image classification method that uses
encrypted images and an isotropic network, such as the vision transformer. The proposed …

The need for cloud servers for training deep neural network (DNN) models is increasing as
more complex architecture designs of DNN models are developed. Nevertheless, cloud …

Privacy preserving machine learning is an active area of research usually relying on
techniques such as homomorphic encryption or secure multiparty computation. Recent …

The acquisition of massive remote sensing data makes it possible to deeply fuse remote
sensing and artificial intelligence (AI). The mobility and cost advantages of new sensing …

In this paper, we propose a novel method for protecting convolutional neural network
models with a secret key set so that unauthorized users without the correct key set cannot …

This paper presents a scrambling inversion attack using a generative adversarial network
(SIA-GAN). This method aims to evaluate the privacy protection level achieved by image …