| Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
| IEICE-SIS, BCT |
2026-10-08
11:25 |
Kagoshima |
Kagoshima Univ.
(Primary: On-site, Secondary: Online) |
A Fundamental Study on Object Hiding Adversarial Attack against Generalizable Neural Radiance Fields
Reo Arimura, Mashiho Mukaida, Satoshi Ono (Kagoshima Univ) |
(To be available after the conference date) [more] |
|
| IEICE-SIS, BCT |
2026-10-08
14:25 |
Kagoshima |
Kagoshima Univ.
(Primary: On-site, Secondary: Online) |
Conditions for Content-Preserving Output Manipulation of Vision-Language Models via Adversarial Images
Naoto Katakai, Ryo Kumagai, Shu Takemoto, Yusuke Nozaki, Masaya Yoshikawa (Meijo Univ.) |
(To be available after the conference date) [more] |
|
| IEICE-ICD, IEICE-SDM, IST [detail] |
2026-08-05
09:45 |
Hokkaido |
Hokkaido Univ, Pharmaceutical Sciences, Lecture Room 2
(Primary: On-site, Secondary: Online) |
[Invited Talk]
High-Performance and Low-Power ADCs Based on Subranging Architecture and Variable-Range Technique
Ritaro Takenaka, Sota Kano, Tetsuya Iizuka (Univ. Tokyo) |
As wireless communication systems continue to increase in speed and density, there is a growing demand for A/D converter... [more] |
|
| ME |
2026-02-28
14:45 |
Online |
online |
A Preliminary Study on Defense Against Adversarial Attack Using Coded Apertures
Yasura Yokogawa (Kagoshima Univ.), Hiroshi Kawasaki (Kyushu Univ.), Hajime Nagahara (Osaka Univ.), Satoshi Ono (Kagoshima Univ.) |
Deep neural networks (DNNs) are known to misclassify adversarial examples (AEs) formed by small perturbations. This stud... [more] |
ME2026-50
pp.47-50 |
| IEICE-IE, ME, IEICE-LOIS, IEE-CMN [detail] |
2025-09-04
16:30 |
Hokkaido |
Hokkaido University of Science
(Primary: On-site, Secondary: Online) |
Manifold oriented Countermeasure against Adversarial Examples
Shunichi Kato, Ryo Kumagai, Shu Takemoto, Yusuke Nozaki, Masaya Yoshikawa (Meijo Univ.) |
Today, AI using DNN is being utilized in all areas of society. However, Adversarial Examples(AE) have been reported as ... [more] |
|
| IST, ME, IEICE-IE, IEICE-BioX, IEICE-SIP, IEICE-MI [detail] |
2025-06-05
15:50 |
Ishikawa |
|
Comparative Analysis of Attack and Defense Methods Against Learned Image Compression Models
Jun Kurihara, Heming Sun (YNU) |
In recent years, learned image compression (LIC) models have attracted attention as a promising approach for achieving h... [more] |
|
| BCT, IEEE-BT |
2025-02-20
16:15 |
Osaka |
(Primary: On-site, Secondary: Online) |
[Memorial Lecture]
Microwave energy transmission by dielectric waveguides
Toshiya Nakagawa, Tsugumi Nishidate, Kazuyuki Saito (Chiba Univ.) |
Microwave energy devices are surgical energy devices excellent for coagulation of biological tissue and are mainly used ... [more] |
BCT2025-31
pp.29-30 |
| IEICE-SIS, BCT |
2024-10-04
13:00 |
Hokkaido |
Hokusei Gakuen Univ.
(Primary: On-site, Secondary: Online) |
Simplified numerical smartphone model for evaluating electromagnetic radiation exposure
-- Application to SAR calculations by use of numerical human body models --
Erina Kawai, Kazuyuki Saito (NICT/Chiba Univ.), Masaharu Takahashi (Chiba Univ.), Tomoaki Nagaoka (NICT) |
With the recent spread of wireless communication devices such as smartphones, it is important to evaluate electromagneti... [more] |
BCT2024-65
pp.29-32 |
| IEICE-ICD, IEICE-SDM, IST [detail] |
2024-08-05
11:25 |
Hokkaido |
(Primary: On-site, Secondary: Online) |
Performance Enhancement and Design Optimization of Analog-to-Digital Converters Utilizing Dynamic Logics
Yuhao Xu, Ritaro Takenaka, Shuowei Li, Haoming Zhang, Tetsuya Iizuka (UTokyo) |
This paper presents a study on performance optimization techniques for SAR ADC by addressing the bottlenecks in speed pe... [more] |
|
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-21
16:15 |
Online |
online |
A Note on Realizing Adversarial Defense Based on Regularization of Multi-stage Squeeze-and-Excitation Features
Jiahuan Zhang, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
Regularizing deep features is a common adversarial defense method. However, the existing methods do not further explore ... [more] |
MMS2022-17 ME2022-42 AIT2022-17
pp.87-90 |
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-21
12:45 |
Online |
online |
Regularizing Generative Adversarial Networks with Internal Representation of Generators
Yusuke Hara, Toshihiko Yamasaki (UTokyo) |
In training generative adversarial networks, maintaining the criteria of the discriminator stably is crucial to training... [more] |
|
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-21
15:20 |
Online |
online |
A Note on Electron Microscope Image Generation from Mix Proportion and Material Property via Generative Adversarial Network for Rubber Materials
Rintaro Yanagi, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
Estimating the properties of rubber materials from ingredients is necessary to accelerate rubber material development. A... [more] |
MMS2022-27 ME2022-52 AIT2022-27
pp.187-191 |
| ME |
2021-12-13
14:50 |
Online |
online |
Latent expression separation using the locality of changes in the real image
Toshiki Hazama (Ritsumeikan Univ.), Masataka Seo (OIT), Yen-Wei Chen (Ritsumeikan Univ.) |
GAN, a deep generative model, makes a great contribution to the image generation task. However, one of the problems that... [more] |
ME2021-95
pp.29-31 |
| ME |
2021-12-13
15:20 |
Online |
online |
Automatic Generation of Viewpoint Change Video Using Consistent Regularized Generative Adversarial Networks
Kento Otsu (Ritsumeikan Univ.), Masataka Seo (Osaka Institute of Technology Osaka), Yen-Wei Chen (Ritsumeikan Univ.) |
Gaze plays an important role in conversation. However, in a video call system using a personal computer or the like, it ... [more] |
ME2021-96
pp.33-35 |
| BCT, IEICE-SIS |
2021-10-07
14:25 |
Online |
online |
Block-wise Transformation with Secret Key for Adversary Robust Defence of SVM model
Ryota Iijima, MaungMaung AprilPyone, Hitoshi Kiya (TMU) |
In this paper, we propose a method for implementing support vector machine (SVM) models that are robust against adversar... [more] |
|
| BCT, IEICE-SIS |
2021-10-08
11:30 |
Online |
online |
Analysis of Writing Style on Wood Slips of the Chinese Han period Using Deep Generative Model
Chiang Meng Yuan, Soh Yoshida, Takao Fujita, Mitsuji Muneyasu (Kansai Univ.) |
In this paper, we develop a method to objectively analyze the calligraphic styles of wood slips excavated in Northwester... [more] |
|
| IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] |
2021-02-19
14:15 |
Online |
Online |
[Special Talk]
A Note on Electron Microscope Image Generation from Mix Proportion via Conditional Style Generative Adversarial Network for Rubber Materials
Rintaro Yanagi, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
Estimating the properties of rubber materials from ingredients is necessary to accelerate rubber material development. I... [more] |
MMS2021-22 ME2021-22 AIT2021-22
pp.171-175 |
| HI, IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] |
2020-02-27
16:20 |
Hokkaido |
Hokkaido Univ.
(Cancelled) |
A Note on Generation of Electron Microscope Images via Auxiliary Classifier Generative Adversarial Network with Mix Proportions
Misaki Kanai, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
In this paper, we investigate a method for generation of images that represent the internal structure of rubber material... [more] |
MMS2020-21 HI2020-21 ME2020-49 AIT2020-21
pp.107-111 |
| HI, IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] |
2020-02-27
14:00 |
Hokkaido |
Hokkaido Univ.
(Cancelled) |
An Image Transformation Network for Privacy-Preserving Deep Neural Networks
Hiroki Ito, Yuma Kinoshita, Hitoshi Kiya (Tokyo Metro. Univ.) |
We propose an image transformation network to generate visually-protected images for privacy-preserving deep neural netw... [more] |
|
| HI, IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] |
2020-02-28
15:10 |
Hokkaido |
Hokkaido Univ.
(Cancelled) |
Unpaired Learning for Noise-free, Scale Invariant, and Interpretable Image Enhancement
Satoshi Kosugi, Toshihiko Yamasaki (Univ. of Tokyo) |
This paper tackles unpaired image enhancement, a task of learning a mapping function which transforms input images into ... [more] |
|