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| Paper Abstract and Keywords | ||
| Presentation | 2022-03-07 17:00 Extention of robust image classification system with Adversarial Example Detectors Miki Tanaka, Takayuki Osakabe, Hitoshi Kiya (Tokyo Metro. Univ.)  EMM2021-105 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In image classification with deep learning, there is a risk that an attacker can intentionally manipulate the prediction results of image classification by using images with a small designed noise, called adversarial examples. In this paper, we propose a robust image classification system against adversarial examples. In order to prevent the attack, there are two approaches: using a robust classifier, and using a detection method of adversarial examples. A robust image classification system with the combination of these two approaches was demonstrated to outperform conventional methods. In this paper, we extend the robust image classification system with the combination of the two approaches by using multiple features. The proposed method is more robust against various adversarial attacks and noise levels than the conventional one. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Adversarial example / Machine learning / Deep learning / Adversarial detection / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 417, EMM2021-105, pp. 76-80, March 2022. | |
| Paper # | EMM2021-105 | |
| Date of Issue | 2022-02-28 (EMM) | |
| 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 | EMM2021-105 |
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| Conference Information | |
| Committee | EMM |
| Conference Date | 2022-03-07 - 2022-03-08 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | (Primary: Online, Secondary: On-site) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Image and Sound Quality, Metrics for Perception and Recognition, Human Auditory and Visual System, etc. |
| Paper Information | |
| Registration To | EMM |
| Conference Code | 2022-03-EMM |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Extention of robust image classification system with Adversarial Example Detectors |
| Sub Title (in English) | |
| Keyword(1) | Adversarial example |
| Keyword(2) | Machine learning |
| Keyword(3) | Deep learning |
| Keyword(4) | Adversarial detection |
| Keyword(5) | |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Miki Tanaka |
| 1st Author's Affiliation | Tokyo Metropolitan University (Tokyo Metro. Univ.) |
| 2nd Author's Name | Takayuki Osakabe |
| 2nd Author's Affiliation | Tokyo Metropolitan University (Tokyo Metro. Univ.) |
| 3rd Author's Name | Hitoshi Kiya |
| 3rd Author's Affiliation | Tokyo Metropolitan University (Tokyo Metro. Univ.) |
| 4th Author's Name | |
| 4th Author's Affiliation | () |
| 5th Author's Name | |
| 5th Author's Affiliation | () |
| 6th Author's Name | |
| 6th Author's Affiliation | () |
| Speaker | Author-1 |
| Date Time | 2022-03-07 17:00:00 |
| Presentation Time | 25 minutes |
| Registration for | EMM |
| Paper # | EMM2021-105 |
| Volume (vol) | vol.121 |
| Number (no) | no.417 |
| Page | pp.76-80 |
| #Pages | 5 |
| Date of Issue | 2022-02-28 (EMM) |
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