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Paper Abstract and Keywords
Presentation 2021-05-21 13:10
A Study of Detecting Adversarial Examples Using Sensitivities to Multiple Auto Encoders
Yuma Yamasaki, Minoru Kuribayashi, Nobuo Funabiki (Okayama Univ.), Huy Hong Nguyen, Isao Echizen (NII) IT2021-11 EMM2021-11
Abstract (in Japanese) (See Japanese page) 
(in English) By removing the small perturbations involved in adversarial examples, the image classification result returns to the correct label of the image, and by gradually increasing the strength of the filter that removes the noise, the image classification result is characterized. In the previous study, we focused on this point and trained a neural network using the image classification results after denoising by each filter with varying the strength as supervisory data to identify adversarial examples. However, since JPEG compression and scaling, which are well-known techniques, are used for denoising filters, the adversarial attack may be adjusted for such filters to fool the detector. In this study, we use an unsupervised machine learning model, Auto Encoder, which is trained on a specific dataset, as a black box filter, to enhance the security aspect. We designed several types of auto encoders with different characteristics by changing the number of images used for training, and evaluated the accuracy of its discrimination capability using each filter alone or in combination. As a result, it was confirmed that the noise removal effect was improved by combining some auto encoders, adversarial examples could be identified with an accuracy of over 90%.
Keyword (in Japanese) (See Japanese page) 
(in English) Adversarial Example / Image Classifier / Auto Encoder / Noise Removal Filter / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 29, EMM2021-11, pp. 60-65, May 2021.
Paper # EMM2021-11 
Date of Issue 2021-05-13 (IT, EMM) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee EMM IT  
Conference Date 2021-05-20 - 2021-05-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information Security, Information Theory, Information Hiding, etc. 
Paper Information
Registration To EMM 
Conference Code 2021-05-EMM-IT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study of Detecting Adversarial Examples Using Sensitivities to Multiple Auto Encoders 
Sub Title (in English)  
Keyword(1) Adversarial Example  
Keyword(2) Image Classifier  
Keyword(3) Auto Encoder  
Keyword(4) Noise Removal Filter  
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1st Author's Name Yuma Yamasaki  
1st Author's Affiliation Okayama University (Okayama Univ.)
2nd Author's Name Minoru Kuribayashi  
2nd Author's Affiliation Okayama University (Okayama Univ.)
3rd Author's Name Nobuo Funabiki  
3rd Author's Affiliation Okayama University (Okayama Univ.)
4th Author's Name Huy Hong Nguyen  
4th Author's Affiliation NII (NII)
5th Author's Name Isao Echizen  
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Speaker
Date Time 2021-05-21 13:10:00 
Presentation Time 25 
Registration for EMM 
Paper # IEICE-IT2021-11,IEICE-EMM2021-11 
Volume (vol) IEICE-121 
Number (no) no.28(IT), no.29(EMM) 
Page pp.60-65 
#Pages IEICE-6 
Date of Issue IEICE-IT-2021-05-13,IEICE-EMM-2021-05-13 


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