17 citations · 57 across the 24 of their papers we have counts for
11 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…
Access Control of Semantic Segmentation Models Using Encrypted Feature Maps
Hiroki Ito, AprilPyone MaungMaung, Sayaka Shiota +1
In this paper, we propose an access control method with a secret key for semantic segmentation models for the first time so that unauthorized users without a secret key cannot bene…
Adversarial Detector with Robust Classifier
Takayuki Osakabe, Maungmaung Aprilpyone, Sayaka Shiota +1
Deep neural network (DNN) models are wellknown to easily misclassify prediction results by using input images with small perturbations, called adversarial examples. In this paper,…
An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applications
Hitoshi Kiya, AprilPyone MaungMaung, Yuma Kinoshita +2
This article presents an overview of image transformation with a secret key and its applications. Image transformation with a secret key enables us not only to protect visual infor…
A universal detector of CNN-generated images using properties of checkerboard artifacts in the frequency domain
Miki Tanaka, Sayaka Shiota, Hitoshi Kiya
We propose a novel universal detector for detecting images generated by using CNNs. In this paper, properties of checkerboard artifacts in CNN-generated images are considered, and…
A Retinex-based Image Enhancement Scheme with Noise Aware Shadow-up Function
Chien Cheng Chien, Yuma Kinoshita, Sayaka Shiota +1
This paper proposes a novel image contrast enhancement method based on both a noise aware shadow-up function and Retinex (retina and cortex) decomposition. Under low light conditio…
A Pseudo Multi-Exposure Fusion Method Using Single Image
Yuma Kinoshita, Sayaka Shiota, Hitoshi Kiya
This paper proposes a novel pseudo multi-exposure image fusion method based on a single image. Multi-exposure image fusion is used to produce images without saturation regions, by…
Multi-Exposure Image Fusion Based on Exposure Compensation
Yuma Kinoshita, Taichi Yoshida, Sayaka Shiota +1
This paper proposes a novel multi-exposure image fusion method based on exposure compensation. Multi-exposure image fusion is a method to produce images without color saturation re…
Super-Resolution using Convolutional Neural Networks without Any Checkerboard Artifacts
Yusuke Sugawara, Sayaka Shiota, Hitoshi Kiya
It is well-known that a number of excellent super-resolution (SR) methods using convolutional neural networks (CNNs) generate checkerboard artifacts. A condition to avoid the check…
Automatic Exposure Compensation for Multi-Exposure Image Fusion
Yuma Kinoshita, Sayaka Shiota, Hitoshi Kiya
This paper proposes a novel luminance adjustment method based on automatic exposure compensation for multi-exposure image fusion. Multi-exposure image fusion is a method to produce…