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Paper Abstract and Keywords
Presentation 2022-06-27 15:30
Adversarial Attacks and Defenses for Non-Parametric Two-Sample Tests
Xilie Xu (NUS), Jingfeng Zhang (RIKEN-AIP), Feng Liu (UTS), Masashi Sugiyama (RIKEN-AIP), Mohan Kankanhalli (NUS) NC2022-4 IBISML2022-4
Abstract (in Japanese) (See Japanese page) 
(in English) When we deploy models trained by standard training (ST), they work well on natural test data. However, those models cannot handle adversarial test data (also known as adversarial examples) that are algorithmically generated by adversarial attacks. An adversarial attack is an algorithm which applies specially designed tiny perturbations on natural data to transform them into adversarial data, in order to mislead a trained model and let it give wrong predictions. Adversarial training (AT) aims at improving the robust accuracy of trained models against adversarial attacks.
In this presentation, we leverage the techniques of AT to evaluate/enhance the reliabilities of some AI tools, such as image denoiser and non-parametric two-sample tests.
Keyword (in Japanese) (See Japanese page) 
(in English) Adversarial robustness / / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 90, IBISML2022-4, pp. 20-46, June 2022.
Paper # IBISML2022-4 
Date of Issue 2022-06-20 (NC, IBISML) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NC2022-4 IBISML2022-4

Conference Information
Committee NC IBISML IPSJ-BIO IPSJ-MPS  
Conference Date 2022-06-27 - 2022-06-29 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To IBISML 
Conference Code 2022-06-NC-IBISML-BIO-MPS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Adversarial Attacks and Defenses for Non-Parametric Two-Sample Tests 
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Keyword(1) Adversarial robustness  
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1st Author's Name Xilie Xu  
1st Author's Affiliation National University of Singapore (NUS)
2nd Author's Name Jingfeng Zhang  
2nd Author's Affiliation RIKEN Center for Advanced Intelligence Project (RIKEN-AIP)
3rd Author's Name Feng Liu  
3rd Author's Affiliation University of Technology Sydney (UTS)
4th Author's Name Masashi Sugiyama  
4th Author's Affiliation RIKEN Center for Advanced Intelligence Project (RIKEN-AIP)
5th Author's Name Mohan Kankanhalli  
5th Author's Affiliation National University of Singapore (NUS)
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Speaker Author-1 
Date Time 2022-06-27 15:30:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # NC2022-4, IBISML2022-4 
Volume (vol) vol.122 
Number (no) no.89(NC), no.90(IBISML) 
Page pp.20-46 
#Pages 27 
Date of Issue 2022-06-20 (NC, IBISML) 

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