2 citations · 3 across the 5 of their papers we have counts for
5 papers
Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformer
Teru Nagamori, Sayaka Shiota, Hitoshi Kiya
We propose a novel method for privacy-preserving deep neural networks (DNNs) with the Vision Transformer (ViT). The method allows us not only to train models and test with visually…
Domain Adaptation for Efficiently Fine-tuning Vision Transformer with Encrypted Images
Teru Nagamori, Sayaka Shiota, Hitoshi Kiya
In recent years, deep neural networks (DNNs) trained with transformed data have been applied to various applications such as privacy-preserving learning, access control, and advers…
Block-Wise Encryption for Reliable Vision Transformer models
Hitoshi Kiya, Ryota Iijima, Teru Nagamori
This article presents block-wise image encryption for the vision transformer and its applications. Perceptual image encryption for deep learning enables us not only to protect the…
Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer
Teru Nagamori, Hitoshi Kiya
In recent years, privacy-preserving methods for deep learning have become an urgent problem. Accordingly, we propose the combined use of federated learning (FL) and encrypted image…
An Access Control Method with Secret Key for Semantic Segmentation Models
Teru Nagamori, Ryota Iijima, Hitoshi Kiya
A novel method for access control with a secret key is proposed to protect models from unauthorized access in this paper. We focus on semantic segmentation models with the vision t…