71 citations · 304 across the 98 of their papers we have counts for
19 papers · 1 filter
On the Security of Pixel-Based Image Encryption for Privacy-Preserving Deep Neural Networks
Warit Sirichotedumrong, Yuma Kinoshita, Hitoshi Kiya
This paper aims to evaluate the safety of a pixel-based image encryption method, which has been proposed to apply images with no visual information to deep neural networks (DNN), i…
Privacy-Preserving Machine Learning Using EtC Images
Ayana Kawamura, Yuma Kinoshita, Hitoshi Kiya
In this paper, we propose a novel privacy-preserving machine learning scheme with encrypted images, called EtC (Encryption-then-Compression) images. Using machine learning algorith…
An Image Fusion Scheme for Single-Shot High Dynamic Range Imaging with Spatially Varying Exposures
Chihiro Go, Yuma Kinoshita, Sayaka Shiota +1
This paper proposes a novel multi-exposure image fusion (MEF) scheme for single-shot high dynamic range imaging with spatially varying exposures (SVE). Single-shot imaging with SVE…
Privacy-Preserving Support Vector Machine Computing Using Random Unitary Transformation
Takahiro Maekawa, Ayana Kawamura, Takayuki Nakachi +1
A privacy-preserving support vector machine (SVM) computing scheme is proposed in this paper. Cloud computing has been spreading in many fields. However, the cloud computing has so…
A hue-preserving tone mapping scheme based on constant-hue plane without gamut problem
Yuma Kinoshita, Kouki Seo, Artit Visavakitcharoen +1
We propose a novel hue-preserving tone mapping scheme. Various tone mapping operations have been studied so far, but there are very few works on color distortion caused in image to…
Single-Shot High Dynamic Range Imaging with Spatially Varying Exposures Considering Hue Distortion
Chihiro Go, Yuma Kinoshita, Sayaka Shiota +1
We proposes a novel single-shot high dynamic range imaging scheme with spatially varying exposures (SVE) considering hue distortion. Single-shot imaging with SVE enables us to capt…
Adversarial Test on Learnable Image Encryption
MaungMaung AprilPyone, Warit Sirichotedumrong, Hitoshi Kiya
Data for deep learning should be protected for privacy preserving. Researchers have come up with the notion of learnable image encryption to satisfy the requirement. However, exist…
A Color Compensation Method Using Inverse Camera Response Function for Multi-exposure Image Fusion
Artit Visavakitcharoen, Yuma Kinoshita, Hitoshi Kiya
Multi-exposure image fusion is a method for producing an image with a wide dynamic range by fusing multiple images taken under various exposure values. In this paper, we discuss co…
Performance Evaluation of Two-layer lossless HDR Coding using Histogram Packing Technique under Various Tone-mapping Operators
Hiroyuki Kobayashi, Hitoshi Kiya
We proposed a lossless two-layer HDR coding method using a histogram packing technique. The proposed method was demonstrated to outperform the normative JPEG XT encoder, under the…
An Image Identification Scheme of Encrypted JPEG Images for Privacy-Preserving Photo Sharing Services
Kenta Iida, Hitoshi Kiya
We propose an image identification scheme for double-compressed encrypted JPEG images that aims to identify encrypted JPEG images that are generated from an original JPEG image. To…
Two-layer Near-lossless HDR Coding with Backward Compatibility to JPEG
Hiroyuki Kobayashi, Osamu Watanabe, Hitoshi Kiya
We propose an efficient two-layer near-lossless coding method using an extended histogram packing technique with backward compatibility to the legacy JPEG standard. The JPEG XT, wh…
Convolutional Neural Networks Considering Local and Global features for Image Enhancement
Yuma Kinoshita, Hitoshi Kiya
In this paper, we propose a novel convolutional neural network (CNN) architecture considering both local and global features for image enhancement. Most conventional image enhancem…
Privacy-Preserving Deep Neural Networks with Pixel-based Image Encryption Considering Data Augmentation in the Encrypted Domain
Warit Sirichotedumrong, Takahiro Maekawa, Yuma Kinoshita +1
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 testi…
HOG feature extraction from encrypted images for privacy-preserving machine learning
Masaki Kitayama, Hitoshi Kiya
In this paper, we propose an extraction method of HOG (histograms-of-oriented-gradients) features from encryption-then-compression (EtC) images for privacy-preserving machine learn…
JPEG XT Image Compression with Hue Compensation for Two-Layer HDR Coding
Hiroyuki Kobayashi, Hitoshi Kiya
We propose a novel JPEG XT image compression with hue compensation for two-layer HDR coding. LDR images produced from JPEG XT bitstreams have some distortion in hue due to tone map…
A Noise-aware Enhancement Method for Underexposed Images
Chien-Cheng Chien, Yuma Kinoshita, Hitoshi Kiya
A novel method of contrast enhancement is proposed for underexposed images, in which heavy noise is hidden. Under low light conditions, images taken by digital cameras have low con…
Scene Segmentation-Based Luminance Adjustment for Multi-Exposure Image Fusion
Yuma Kinoshita, Hitoshi Kiya
We propose a novel method for adjusting luminance for multi-exposure image fusion. For the adjustment, two novel scene segmentation approaches based on luminance distribution are a…
Deep Inverse Tone Mapping Using LDR Based Learning for Estimating HDR Images with Absolute Luminance
Yuma Kinoshita, Hitoshi Kiya
In this paper, a novel inverse tone mapping method using a convolutional neural network (CNN) with LDR based learning is proposed. In conventional inverse tone mapping with CNNs, g…
Image Enhancement Network Trained by Using HDR images
Yuma Kinoshita, Hitoshi Kiya
In this paper, a novel image enhancement network is proposed, where HDR images are used for generating training data for our network. Most of conventional image enhancement methods…