BZPEERPapers, people, evidence and research paths
Menu

Discover

SearchFind papers, people, topics, institutions and opportunities from one place.GraphSee how papers, authors, topics and evidence connect.

Research tools

HomeContinue from the papers, people and topics that matter now.CollectionsTurn saved papers into reading lists, evidence maps and shareable context.ResearchReconstruct the path that led you from search to paper to question.CollaborationMove from a paper, topic or expert request into a focused thread.PublishCreate full-text research articles with metadata, formulas and versions.

Network

ExpertsFind specialists whose work and reviews explain why they can help.CommunitiesJoin research groups organized around papers, topics and shared evidence.

Opportunities

ListingsBrowse roles, calls and collaborations connected to real research context.

Account

Log inReturn to your saved papers, graph views and active threads.Create accountStart saving papers, building a research trail and joining teams.
Log inCreate account

Source author record

Tatsuya Chuman

Tatsuya Chuman appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
Cryptography and SecurityComputer Vision

Catalog footprint

What is connected

4works
2topics
3close collaborators

Actions

Connect this record

Log in to claim
Open graphBrowse works

Research graph

See the researcher in context

Open full explorer

Inspect adjacent papers, topics, institutions and collaborators without losing the researcher page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

4 published item(s)

preprint2026arXiv

Privacy-Preserving Clothing Classification using Vision Transformer for Thermal Comfort Estimation

A privacy-preserving clothing classification scheme is presented to enable secure occupant-centric control (OCC) systems. Although the utilization of camera images for HVAC control has been widely studied to optimize thermal comfort, privacy protection of occupant images has not been considered in prior works. While various privacy-preserving methods have been proposed for image classification, applying conventional schemes results in severe accuracy degradation. In this paper, we introduce a privacy-preserving classification method using Vision Transformer (ViT) applied to clothing insulation estimation. In an experiment using the DeepFashion dataset categorized by clothing insulation, while the conventional pixel-based method suffers a severe accuracy drop, our scheme maintains a high accuracy on encrypted images, showing no degradation from plain images across all categories.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

A Privacy Preserving Method with a Random Orthogonal Matrix for ConvMixer Models

In this paper, a privacy preserving image classification method is proposed under the use of ConvMixer models. To protect the visual information of test images, a test image is divided into blocks, and then every block is encrypted by using a random orthogonal matrix. Moreover, a ConvMixer model trained with plain images is transformed by the random orthogonal matrix used for encrypting test images, on the basis of the embedding structure of ConvMixer. The proposed method allows us not only to use the same classification accuracy as that of ConvMixer models without considering privacy protection but to also enhance robustness against various attacks compared to conventional privacy-preserving learning.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Security Evaluation of Block-based Image Encryption for Vision Transformer against Jigsaw Puzzle Solver Attack

The aim of this paper is to evaluate the security of a block-based image encryption for the vision transformer against jigsaw puzzle solver attacks. The vision transformer, a model for image classification based on the transformer architecture, is carried out by dividing an image into a grid of square patches. Some encryption schemes for the vision transformer have been proposed by applying block-based image encryption such as block scrambling and rotating to patches of the image. On the other hand, the security of encryption scheme for the vision transformer has never evaluated. In this paper, jigsaw puzzle solver attacks are utilized to evaluate the security of encrypted images by regarding the divided patches as pieces of a jigsaw puzzle. In experiments, an image is resized and divided into patches to apply block scrambling-based image encryption, and then the security of encrypted images for the vision transformer against jigsaw puzzle solver attacks is evaluated.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Security Evaluation of Compressible Image Encryption for Privacy-Preserving Image Classification against Ciphertext-only Attacks

The security of learnable image encryption schemes for image classification using deep neural networks against several attacks has been discussed. On the other hand, block scrambling image encryption using the vision transformer has been proposed, which applies to lossless compression methods such as JPEG standard by dividing an image into permuted blocks. Although robustness of the block scrambling image encryption against jigsaw puzzle solver attacks that utilize a correlation among the blocks has been evaluated under the condition of a large number of encrypted blocks, the security of encrypted images with a small number of blocks has never been evaluated. In this paper, the security of the block scrambling image encryption against ciphertext-only attacks is evaluated by using jigsaw puzzle solver attacks.

0 citations0 reviewsNo code linkedOpen access