2 citations · 4 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
Access Control with Encrypted Feature Maps for Object Detection Models
Teru Nagamori, Hiroki Ito, AprilPyone MaungMaung +1
In this paper, we propose an access control method with a secret key for object detection models for the first time so that unauthorized users without a secret key cannot benefit f…
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…
Access Control of Object Detection Models Using Encrypted Feature Maps
Teru Nagamori, Hiroki Ito, April Pyone Maung Maung +1
In this paper, we propose an access control method for object detection models. The use of encrypted images or encrypted feature maps has been demonstrated to be effective in acces…