Paper Abstract and Keywords |
Presentation |
2022-05-17 15:05
Generating patch-wise adversarial examples for avoidance of face recognition system and verification of its robustness Hiroto Takiwaki, Minoru Kuribayashi, Nobuo Funabiki (Okayama univ.) IT2022-5 EMM2022-5 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Advances in machine learning technologies such as Convolutional Neural Networks (CNN) have made it possible to identify individuals by face recognition. The system can be constructed to detect the presence of a specific type of object. However, if these technologies are abused, individuals can be automatically tracked and their privacy will be violated. Therefore, it is necessary to develop a technique for avoiding automated human tracking systems based on a face identification technique. Altough it has been considered to generate an adversarial noise to avoid automated tracking, changing position and size of the generated noise has not been examined in detail. In this paper, we use a small and transparent patch to generate adversarial examples by adding noise at various positions and sizes, and evaluated its performance. In addition, the robustness of the system against disturbance is considered by shifting the position of the attachment. The experimental results show that adversarial exanples can be generated for various parts of the face. The attack success rate is high when the noise is close to the facial features such as eyes, nose, and chin. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Face recognition / Adversarial example / FGSM / robustness / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 26, EMM2022-5, pp. 23-28, May 2022. |
Paper # |
EMM2022-5 |
Date of Issue |
2022-05-10 (IT, 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) |
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IT2022-5 EMM2022-5 |
Conference Information |
Committee |
IT EMM |
Conference Date |
2022-05-17 - 2022-05-18 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Gifu University |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Information Security, Information Theory, Information Hiding, etc. |
Paper Information |
Registration To |
EMM |
Conference Code |
2022-05-IT-EMM |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Generating patch-wise adversarial examples for avoidance of face recognition system and verification of its robustness |
Sub Title (in English) |
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Face recognition |
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Adversarial example |
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FGSM |
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robustness |
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1st Author's Name |
Hiroto Takiwaki |
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 |
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Okayama University (Okayama univ.) |
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Speaker |
Author-1 |
Date Time |
2022-05-17 15:05:00 |
Presentation Time |
25 minutes |
Registration for |
EMM |
Paper # |
IT2022-5, EMM2022-5 |
Volume (vol) |
vol.122 |
Number (no) |
no.25(IT), no.26(EMM) |
Page |
pp.23-28 |
#Pages |
6 |
Date of Issue |
2022-05-10 (IT, EMM) |
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