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| Paper Abstract and Keywords | ||
| Presentation | 2022-06-27 15:30 Evaluating and Enhancing Reliabilities of AI-Powered Tools -- Adversarial Robustness -- Jingfeng Zhang (RIKEN-AIP)  NC2022-4 IBISML2022-4 |
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| 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. |
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| 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 |
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| 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) | Evaluating and Enhancing Reliabilities of AI-Powered Tools |
| Sub Title (in English) | Adversarial Robustness |
| Keyword(1) | Adversarial robustness |
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| 1st Author's Name | Jingfeng Zhang |
| 1st Author's Affiliation | RIKEN Center for Advanced Intelligence Project (RIKEN-AIP) |
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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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