Adversarial Image Translation: Unrestricted Adversarial Examples in Face Recognition Systems (Record no. 651)

000 -LEADER
fixed length control field 01428nam a2200169 a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260901030334.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250101s2019 xx o 000 0 eng d
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Kazuya Kakizaki
245 10 - TITLE STATEMENT
Title Adversarial Image Translation: Unrestricted Adversarial Examples in Face Recognition Systems
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Name of producer, publisher, distributor, manufacturer arXiv
Date of production, publication, distribution, manufacture, or copyright notice 2019
336 ## - CONTENT TYPE
Content type term text
338 ## - CARRIER TYPE
Carrier type term online resource
520 ## - SUMMARY, ETC.
Summary, etc. Thanks to recent advances in deep neural networks (DNNs), face recognition systems have become highly accurate in classifying a large number of face images. However, recent studies have found that DNNs could be vulnerable to adversarial examples, raising concerns about the robustness of such systems. Adversarial examples that are not restricted to small perturbations could be more serious since conventional certified defenses might be ineffective against them. To shed light on the vulnerability to such adversarial examples, we propose a flexible and efficient method for generating unrestricted adversarial examples using image translation techniques. Our method enables us to translate a source image into any desired facial appearance with large perturbations to deceive target face recognition systems. Our experimental results indicate that our method achieved about $90$ and $80\%$ attack
506 0# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Open access — freely available to read.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://arxiv.org/pdf/1905.03421v3">https://arxiv.org/pdf/1905.03421v3</a>
Link text Read the full paper (PDF)
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Electronic resource (link)
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Withdrawn status Lost status Damaged status Not for loan Collection Home library Current library Shelving location Date acquired Total checkouts Barcode Date last seen Uniform resource identifier Price effective from Koha item type
      Available online Cybersecurity Yegates University Library Yegates University Library Science and Computing 09/01/2026   YGE000912 09/01/2026 https://arxiv.org/pdf/1905.03421v3 09/01/2026 Research paper — read online