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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-02 11:40 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Novel Adversarial Attacks Based on Embedding Geometry of Data Manifolds Masahiro Morita, Hajime Tasaki, Jinhui Chao (Chuo Univ.)  PRMU2022-84 IBISML2022-91 |
It has been shown recently that adversarial examples inducing misclassification by deep neural networks exist in the ort... [more] | PRMU2022-84 IBISML2022-91 pp.140-145 |
| SIS | 2023-03-02 13:30 |
Chiba | Chiba Institute of Technology (Primary: On-site, Secondary: Online) |
An image watermarking method using adversarial perturbations Sei Takano, Mitsuji Muneyasu, Soh Yoshida (Kansai Univ.) SIS2022-43 |
The performance of convolutional neural networks (CNNs) has been dramatically improved in recent years, and they have at... [more] | SIS2022-43 pp.15-20 |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2023-02-22 09:45 |
Hokkaido | Hokkaido Univ. | Probabilistic Approach towards Theoretical Understanding for Adversarial Training Soichiro Kumano (UTokyo), Hiroshi Kera (Chiba Univ.), Toshihiko Yamasaki (UTokyo) ITS2022-59 IE2022-76 |
In this paper, we provide the first theoretical analysis of the training dynamics of adversarial training of deep neural... [more] | ITS2022-59 IE2022-76 pp.95-100 |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2023-02-22 10:15 |
Hokkaido | Hokkaido Univ. | Generation Method of Targeted Adversarial Examples using Gradient Information for the Target Class of the Image Ryo Kumagai, Shu Takemoto, Yusuke Nozaki, Masaya Yoshikawa (Meijo Univ.) ITS2022-61 IE2022-78 |
With the advancement of AI technology, the vulnerability of AI system is pointed out. The adversarial examples (AE), whi... [more] | ITS2022-61 IE2022-78 pp.107-111 |
| EMM | 2023-01-26 09:55 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
On the Transferability of Adversarial Examples between Isotropic Network and CNN models Miki Tanaka (Tokyo Metropolitan Univ.), Isao Echizen (NII), Hitoshi Kiya (Tokyo Metropolitan Univ.) EMM2022-62 |
Deep neural networks are well known to be vulnerable to adversarial examples (AEs). In addition, AEs generated for a sou... [more] | EMM2022-62 pp.7-12 |
| SIS | 2022-12-05 15:10 |
Osaka | (Primary: On-site, Secondary: Online) |
Application of Adversarial Training in Detection of Calcification Regions from Dental Panoramic Radiographs Sei Takano, Mitsuji Muneyasu, Soh Yoshida, Akira Asano (Kansai Univ.), Keiichi Uchida (Matsumoto Dental Univ. Hospital) SIS2022-28 |
Calcification regions that are a sign of vascular diseases may be observed on dental panoramic radiographs. The finding ... [more] | SIS2022-28 pp.26-31 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM [detail] | 2022-11-30 16:40 |
Kumamoto | (Primary: On-site, Secondary: Online) |
Evaluation of Model Quantization Method on Vitis-AI for Mitigating Adversarial Examples Yuta Fukuda, Kota Yoshida, Takeshi Fujino (Ritsumeikan Univ.) VLD2022-51 ICD2022-68 DC2022-67 RECONF2022-74 |
Adversarial examples (AEs) are security threats in deep neural networks (DNNs). One of the countermeasures is adversaria... [more] | VLD2022-51 ICD2022-68 DC2022-67 RECONF2022-74 pp.182-187 |
| HWS, ICD | 2022-10-25 13:50 |
Shiga | (Primary: On-site, Secondary: Online) |
Fundamental Study of Adversarial Examples Created by Fault Injection Attack on Image Sensor Interface Tatsuya Oyama, Kota Yoshida, Shunsuke Okura, Takeshi Fujino (Ritsumeikan Univ.) HWS2022-36 ICD2022-28 |
Adversarial examples (AEs), which cause misclassification by adding subtle perturbations to input images, have been prop... [more] | HWS2022-36 ICD2022-28 pp.35-40 |
| SIP | 2022-08-26 14:26 |
Okinawa | Nobumoto Ohama Memorial Hall (Ishigaki Island) (Primary: On-site, Secondary: Online) |
Generation method of Adversarial Examples using XAI Ryo Kumagai, Shu Takemoto, Yusuke Nozaki, Masaya Yoshikawa (Meijo Univ.) SIP2022-72 |
With the advancement of AI technology, AI can be applied to various fields. Therefore the accountability for the decisio... [more] | SIP2022-72 pp.115-120 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2022-06-27 15:30 |
Okinawa | (Primary: On-site, Secondary: Online) |
Evaluating and Enhancing Reliabilities of AI-Powered Tools
-- Adversarial Robustness -- Jingfeng Zhang (RIKEN-AIP) NC2022-4 IBISML2022-4 |
When we deploy models trained by standard training (ST), they work well on natural test data. However, those models cann... [more] | NC2022-4 IBISML2022-4 pp.20-46 |
| IA, ICSS | 2022-06-24 10:25 |
Nagasaki | Univ. of Nagasaki (Primary: On-site, Secondary: Online) |
Application of Adversarial Examples to Physical ECG Signals Taiga Ono (Waseda Univ.), Takeshi Sugawara (UEC), Jun Sakuma (Tsukuba Univ./RIKEN), Tatsuya Mori (Waseda Univ./RIKEN/NICT) IA2022-11 ICSS2022-11 |
This work aims to assess the reality and feasibility of applying adversarial examples to attack cardiac diagnosis system... [more] | IA2022-11 ICSS2022-11 pp.61-66 |
| CAS, SIP, VLD, MSS | 2022-06-16 14:40 |
Aomori | Hachinohe Institute of Technology (Primary: On-site, Secondary: Online) |
Adversarial Robustness of Secret Key-Based Defenses against AutoAttack Miki Tanaka, April Pyone MaungMaung (Tokyo Metro Univ.), Isao Echizen (NII), Hitoshi Kiya (Tokyo Metro Univ.) CAS2022-7 VLD2022-7 SIP2022-38 MSS2022-7 |
Deep neural network (DNN) models are well-known to easily misclassify prediction results by using input images with smal... [more] | CAS2022-7 VLD2022-7 SIP2022-38 MSS2022-7 pp.34-39 |
| IT, EMM | 2022-05-17 15:05 |
Gifu | Gifu University (Primary: On-site, Secondary: Online) |
Generating patch-wise adversarial examples for avoidance of face recognition system and verification of its robustness Hiroto Takiwaki, Minoru Kuribayashi, Nobuo Funabiki (Okayama univ.) IT2022-5 EMM2022-5 |
Advances in machine learning technologies such as Convolutional Neural Networks (CNN) have made it possible to identify ... [more] | IT2022-5 EMM2022-5 pp.23-28 |
| IT, EMM | 2022-05-17 15:30 |
Gifu | Gifu University (Primary: On-site, Secondary: Online) |
A study of adversarial example detection using the correlation between adversarial noise and JPEG compression-derived distortion Kenta Tsunomori, Yuma Yamasaki, Minoru Kuribayashi, Nobuo Funabiki (Okayama Univ.), Isao Echizen (NII) IT2022-6 EMM2022-6 |
Adversarial examples cause misclassification of image classifiers. Higashi et al. proposed a method to detect adversari... [more] | IT2022-6 EMM2022-6 pp.29-34 |
| PRMU, IPSJ-CVIM | 2022-03-10 17:30 |
Online | Online | Adversarial Training: A Survey Hiroki Adachi, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2021-73 |
Adversarial training (AT) is a training method that aims to obtain a robust model for defencing the adversarial attack b... [more] | PRMU2021-73 pp.78-90 |
| EMM | 2022-03-07 15:25 |
Online | (Primary: Online, Secondary: On-site) (Primary: Online, Secondary: On-site) |
[Poster Presentation]
A Proposal for Emotion-Expressive Editor:EmoEditor by Font Changing Yuuki Shimamura, Michiharu Niimi (KIT) EMM2021-100 |
Text media is one of important ways in communications on computers. For example, email, LINE or Twitter uses it frequent... [more] | EMM2021-100 pp.46-51 |
| EMM | 2022-03-07 17:00 |
Online | (Primary: Online, Secondary: On-site) (Primary: Online, Secondary: On-site) |
Extention of robust image classification system with Adversarial Example Detectors Miki Tanaka, Takayuki Osakabe, Hitoshi Kiya (Tokyo Metro. Univ.) EMM2021-105 |
In image classification with deep learning, there is a risk that an attacker can intentionally manipulate the prediction... [more] | EMM2021-105 pp.76-80 |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-23 11:45 |
Online | Online | Adversarial Training with Knowledge Distillation considering Intermediate Feature Representation in CNNs Hikaru Higuchi (The Univ. of Electro-Communications), Satoshi Suzuki (former NTT), Hayaru Shouno (The Univ. of Electro-Communications) NC2021-44 |
Adversarial examples are one of the vulnerability attacks to the convolution neural network (CNN). The adversarialexampl... [more] | NC2021-44 pp.59-64 |
| IBISML | 2022-01-18 14:00 |
Online | Online | Robustness to Adversarial Examples by Mixtures of L1 Regularazation Models Hironobu Takenouchi, Junichi Takeuchi (Kyushu Univ.) IBISML2021-26 |
We propose a method of adversarial training using L1 regularizationfor image classification.It is known that L1 regulari... [more] | IBISML2021-26 pp.61-66 |
| MIKA (3rd) |
2021-10-28 10:30 |
Okinawa | (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Examination of Majority Decision Method for Network Intrusion Detection System Using Deep Learning Koko Nishiura, Yuju Ogawa, Tomotaka Kimura, Jun Cheng (Doshisha Univ.) |
In recent years, the importance of NIDS (Network Intrusion Detection Systems), which detects unauthorized access, has be... [more] | |
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