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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| EMM | 2021-03-04 14:15 |
Online | Online | [Poster Presentation]
Detection of Adversarial Examples in CNN Image Classifiers Using Features Extracted with Multiple Strengths of Filter Akinori Higashi, Minoru Kuribayashi, Nobuo Funabiki (Okayama Univ.), Huy Hong Nguyen, Isao Echizen (NII)  EMM2020-70 |
Deep learning has been used as a new method for machine learning, and its performance has been significantly improved. A... [more] | EMM2020-70 pp.19-24 |
| PRMU, IPSJ-CVIM | 2021-03-05 16:25 |
Online | Online | Towards Adversarial Robustness of Learning in the Frequency Domain Subhajit Chaudhury, Toshihiko Yamasaki (UTokyo) PRMU2020-100 |
Adversarial attacks study the effect of noise on the robustness of Convolutional Neural Networks (CNNs). Typically, thes... [more] | PRMU2020-100 pp.176-180 |
| ICSS, IPSJ-SPT | 2021-03-02 13:40 |
Online | Online | Research on the vulnerability of homoglyph attacks to online machine translation system Takeshi Sakamoto, Tatsuya Mori (Waseda Univ) ICSS2020-50 |
It has been widely known that systems empowered by neural network algorithms are vulnerable against an intrinsic attack ... [more] | ICSS2020-50 pp.144-149 |
| BioX | 2020-11-25 11:10 |
Online | Online | GAN based feature-level supportive method for improved adversarial attacks on face recognition Zhengwei Yin (USTC/Hosei Univ.), Kaoru Uchida (Hosei Univ.) BioX2020-35 |
With the rapid development of deep neural networks (DNN), DNN-based face recognition technologies are also achieving gre... [more] | BioX2020-35 pp.1-6 |
| SITE, ISEC, HWS, EMM, BioX, IPSJ-CSEC, IPSJ-SPT, ICSS [detail] | 2020-07-21 10:50 |
Online | Online | Adversarial scan attack against ICP algorithm for pose estimation on LiDAR-based SLAM Kota Yoshida, Takeshi Fujino (Ritsumeikan Univ.) ISEC2020-26 SITE2020-23 BioX2020-29 HWS2020-19 ICSS2020-13 EMM2020-23 |
An autonomous robot is controlled on physical information acquired by various sensors. Some physical attacks are propose... [more] | ISEC2020-26 SITE2020-23 BioX2020-29 HWS2020-19 ICSS2020-13 EMM2020-23 pp.81-86 |
| IBISML | 2020-03-11 15:10 |
Kyoto | Kyoto University (Cancelled but technical report was issued) |
Fairness Causes Vulnerability to Adversarial Attacks Koki Wataoka, Takashi Matsubara, Kuniaki Uehara (Kobe Univ.) IBISML2019-48 |
When using machine learning models in society, it is essential to be ensure classifiers are fair to race and gender. In ... [more] | IBISML2019-48 pp.101-105 |
| SP, EA, SIP | 2020-03-02 13:00 |
Okinawa | Okinawa Industry Support Center (Cancelled but technical report was issued) |
Vulnerability investigation of speaker verification against black-box adversarial attacks Hiroto Kai, Sayaka Shiota, Hitoshi Kiya (TMU) EA2019-106 SIP2019-108 SP2019-55 |
Recently，vulnerability against adversarial attacks is being feared for machine learning-based systems．Adversarial attack... [more] | EA2019-106 SIP2019-108 SP2019-55 pp.29-33 |
| ICSS, IPSJ-SPT | 2020-03-03 11:20 |
Okinawa | Okinawa-Ken-Seinen-Kaikan (Cancelled but technical report was issued) |
Adversarial Attack against Neural Machine Translation Systems Takeshi Sakamoto, Tatsuya Mori (Waseda Univ.) ICSS2019-89 |
It has been widely known that systems empowered by neural network algorithms are vulnerable against an intrinsic attack ... [more] | ICSS2019-89 pp.125-130 |
| ICSS, IPSJ-SPT | 2020-03-03 11:40 |
Okinawa | Okinawa-Ken-Seinen-Kaikan (Cancelled but technical report was issued) |
Adversarial Attacks against Electrocardiograms Taiga Ono (Waseda Univ.), Takeshi Sugawara (UEC), Tatsuya Mori (Waseda Univ.) ICSS2019-90 |
Recent advancements in clinical services powered by deep learning have been met with the threat of Adversarial Examples.... [more] | ICSS2019-90 pp.131-136 |
| IE, CS, IPSJ-AVM, ITE-BCT [detail] | 2019-12-06 10:10 |
Iwate | Aiina Center | Adversarial Examples for Monocular Depth Estimation Koichiro Yamanaka, Ryutaroh Matsumoto, Keita Takahashi, Toshiaki Fujii (Nagoya Univ.) CS2019-83 IE2019-63 |
Adversarial examples for classification and object recognition problems using convolutional neural net- works (CNN) have... [more] | CS2019-83 IE2019-63 pp.91-95 |
| ISEC, SITE, LOIS | 2019-11-02 15:00 |
Osaka | Osaka Univ. | On Robustness of Machine-Learning-Based Malware Detection Wanjia Zheng (U. Tsukuba), Kazumasa Omote (U. Tsukuba/NICT) ISEC2019-83 SITE2019-77 LOIS2019-42 |
As the 2020 Tokyo Olympics are approaching, the possibility of being targeted by attackers has further increased in Japa... [more] | ISEC2019-83 SITE2019-77 LOIS2019-42 pp.133-140 |
| IN, NS (Joint) |
2019-03-04 09:00 |
Okinawa | Okinawa Convention Center | Intrusion Detection System using semi-supervised learning with Adversarial Autoencoder Kazuki Hara, Kohei Shiomoto (Tokyo City Univ.) NS2018-193 |
In recent years the importance of intrusion detection system(IDS) is increasing. In particular, a method using machine l... [more] | NS2018-193 pp.1-6 |
| ISEC | 2007-09-07 15:45 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | An Approach to Duality in Public Key Cryptosystems Kazuo Ohta (UEC), Yuichi Komano (Toshiba), Yutaka Kawai (UEC), Shinichi Kawamura (Toshiba) ISEC2007-87 |
The security of cryptosystems is formalized by the combination of adversarial goal GOAL and attack model ATK. Paillier a... [more] | ISEC2007-87 pp.99-106 |
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