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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 52  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
ICSS, IPSJ-SPT 2023-03-13
Okinawa Okinawaken Seinenkaikan
(Primary: On-site, Secondary: Online)
Dynamic Analysis of Adversarial Attacks
Kentaro Goto (JPNIC), Masato Uchida (Waseda Univ.)
(To be available after the conference date) [more]
PRMU, IBISML, IPSJ-CVIM [detail] 2023-03-02
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.)
(To be available after the conference date) [more]
SIS 2023-03-02
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.)
(To be available after the conference date) [more]
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] 2023-02-22
Hokkaido Hokkaido Univ. Probabilistic Approach towards Theoretical Understanding for Adversarial Training
Socihiro Kumano (UTokyo), Hiroshi Kera (Chiba Univ.), Toshihiko Yamasaki (UTokyo)
(To be available after the conference date) [more]
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] 2023-02-22
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.)
(To be available after the conference date) [more]
EMM 2023-01-26
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
SIS 2022-12-05
(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
VLD, DC, RECONF, ICD, IPSJ-SLDM [detail] 2022-11-30
Ishikawa Kanazawa Bunka Hall
(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
HWS, ICD 2022-10-25
(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
SIP 2022-08-26
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
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] 2022-06-27
(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
IA, ICSS 2022-06-24
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
CAS, SIP, VLD, MSS 2022-06-16
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
IT, EMM 2022-05-17
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
IT, EMM 2022-05-17
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
PRMU, IPSJ-CVIM 2022-03-10
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
EMM 2022-03-07
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
EMM 2022-03-07
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
(Joint) [detail]
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
IBISML 2022-01-18
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
 Results 1 - 20 of 52  /  [Next]  
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