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Black-box Evolutionary Search for Adversarial Examples against Deep Image Classifiers in Non-Targeted Attacks
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SYSNO ASEP 0534345 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Black-box Evolutionary Search for Adversarial Examples against Deep Image Classifiers in Non-Targeted Attacks Author(s) Procházka, Štěpán (UIVT-O)
Neruda, Roman (UIVT-O) SAI, RID, ORCIDArticle number 9207688 Source Title 2020 International Joint Conference on Neural Networks (IJCNN): Conference Proceedings. - Piscataway : IEEE, 2020 - ISBN 978-1-7281-6926-2 Number of pages 8 s. Publication form Online - E Action IJCNN 2020: International Joint Conference on Neural Networks /33./ Event date 19.07.2020 - 24.07.2020 VEvent location Glasgow Country GB - United Kingdom Event type WRD Language eng - English Country US - United States Keywords Machine learning ; Perturbation methods ; Task analysis ; Data models ; Computer architecture ; Sociology ; Statistics Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA18-23827S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 UT WOS 000626021408068 EID SCOPUS 85093844905 DOI 10.1109/IJCNN48605.2020.9207688 Annotation Machine learning models exhibit vulnerability to adversarial examples i.e., artificially created inputs that become misinterpreted. The goal of this paper is to explore non-targeted black-box adversarial attacks on deep networks performing image classification. The original evolutionary algorithm for generating adversarial examples is proposed that employs a guided multi-objective search through the space of perturbed images. The efficiency of attacks is validated by experiments with the CIFAR-10 data set. The experimental results verify the usability of our approach against deep convolutional neural networks. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2021
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