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| Presentation | 2020-03-02 11:50 Generating Adversarial Videos that Bypass Content Filtering Norihito Omori, Tatsuya Mori (Waseda Univ.)  ICSS2019-95 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | A huge number of user-generated contents (UGC) are being uploaded on prominent internet video sharing sites such as YouTube. Such UGC includes many inappropriate content such as violence and discrimination. As a promising approach to automatically detect such inappropriate content from a huge volume of uploaded movies, machine-learning approaches using neural networks are increasingly becoming popular. On the other hand, it is well known that neural networks have an inherent vulnerability called ``adversarial input''. That is, it is possible to intentionally let a neural network misclassify an input by generating adversarial inputs. In this paper, we attempt to study whether an attacker can generate adversarial inputs against a neural network-based inappropriate content filtering system. This paper studies how to construct an effective adversarial input for a white box implementation that identifies whether or not an uploaded video contains violent scenes. We also perform user study that aims to study how generated adversarial input is perceived by human. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | video content filtering / adversarial input / neural network / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 437, ICSS2019-95, pp. 201-206, March 2020. | |
| Paper # | ICSS2019-95 | |
| Date of Issue | 2020-02-24 (ICSS) | |
| 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 | ICSS2019-95 |
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| Conference Information | |
| Committee | ICSS IPSJ-SPT |
| Conference Date | 2020-03-02 - 2020-03-03 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okinawa-Ken-Seinen-Kaikan |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Security, Trust, etc. |
| Paper Information | |
| Registration To | ICSS |
| Conference Code | 2020-03-ICSS-SPT |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Generating Adversarial Videos that Bypass Content Filtering |
| Sub Title (in English) | |
| Keyword(1) | video content filtering |
| Keyword(2) | adversarial input |
| Keyword(3) | neural network |
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| 1st Author's Name | Norihito Omori |
| 1st Author's Affiliation | Waseda University (Waseda Univ.) |
| 2nd Author's Name | Tatsuya Mori |
| 2nd Author's Affiliation | Waseda University (Waseda Univ.) |
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| Speaker | Author-1 |
| Date Time | 2020-03-02 11:50:00 |
| Presentation Time | 20 minutes |
| Registration for | ICSS |
| Paper # | ICSS2019-95 |
| Volume (vol) | vol.119 |
| Number (no) | no.437 |
| Page | pp.201-206 |
| #Pages | 6 |
| Date of Issue | 2020-02-24 (ICSS) |
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