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| Presentation | 2021-05-19 15:30 [Invited Talk] Simple Electromagnetic Analysis Against Activation Functions of Deep Neural Networks (from AIHWS 2020) Go Takatoi, Takeshi Sugawara, Kazuo Sakiyama (UEC), Yuko Hara-Azumi (Tokyo Tech), Yang Li (UEC)  ISEC2021-9 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This invited abstract is based on the papers [1] and [2]. There are physical attacks such as side-channel attacks that are highly effective against edge artificial intelligence due to its property of the device computing close to the user. A previous research by Batina et al. has exploited this characteristic and reverse engineered a black box neural network [3]. We have studied an alternative method to retrieve the activation functions of a black box neural network implemented on to an edge device by using simple electromagnetic analysis and signal processing. Our attack discerns activation functions by observing the relation between the operations in the activation function and electromagnetic (EM) traces. The proposed method requires few executions and inputs, and also has little implementation dependency on activation functions. We have attempted to distinguish eight activation functions with EM measurements, and examined the versatility and limits of this attack. In this work, the machine learning architecture is a multilayer perceptron, and evaluated on an Arduino Uno. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Machine Learning / Deep Learning / Side-channel / Activation Function / Simple Electromagnetic Analysis / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 22, ISEC2021-9, pp. 34-34, May 2021. | |
| Paper # | ISEC2021-9 | |
| Date of Issue | 2021-05-12 (ISEC) | |
| 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 | ISEC2021-9 |
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| Conference Information | |
| Committee | ISEC |
| Conference Date | 2021-05-19 - 2021-05-19 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | ISEC |
| Conference Code | 2021-05-ISEC |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Simple Electromagnetic Analysis Against Activation Functions of Deep Neural Networks (from AIHWS 2020) |
| Sub Title (in English) | |
| Keyword(1) | Machine Learning |
| Keyword(2) | Deep Learning |
| Keyword(3) | Side-channel |
| Keyword(4) | Activation Function |
| Keyword(5) | Simple Electromagnetic Analysis |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Go Takatoi |
| 1st Author's Affiliation | University of Electro-Communications (UEC) |
| 2nd Author's Name | Takeshi Sugawara |
| 2nd Author's Affiliation | University of Electro-Communications (UEC) |
| 3rd Author's Name | Kazuo Sakiyama |
| 3rd Author's Affiliation | University of Electro-Communications (UEC) |
| 4th Author's Name | Yuko Hara-Azumi |
| 4th Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
| 5th Author's Name | Yang Li |
| 5th Author's Affiliation | University of Electro-Communications (UEC) |
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| Speaker | Author-1 |
| Date Time | 2021-05-19 15:30:00 |
| Presentation Time | 30 minutes |
| Registration for | ISEC |
| Paper # | ISEC2021-9 |
| Volume (vol) | vol.121 |
| Number (no) | no.22 |
| Page | p.34 |
| #Pages | 1 |
| Date of Issue | 2021-05-12 (ISEC) |
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