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Adversarial Examples in Consumer Electronics: Deceptive Threats to AI Systems
Yang Ming Chiao Tung University, Hsinchu, Taiwan, 30010.ORCID iD: 0000-0002-1677-2131
National Ilan University, Yilan, Taiwan, 260.ORCID iD: 0000-0001-7163-6928
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-2763-8085
Flinders University, Bedford Park, SA, Australia, 5042.ORCID iD: 0000-0001-7818-459X
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2025 (English)In: IEEE Consumer Electronics Magazine, ISSN 2162-2248, E-ISSN 2162-2256, Vol. 14, no 3, p. 25-26Article in journal, Editorial material (Other academic) Published
Abstract [en]

Artificial intelligence (AI) has become a cornerstone of modern consumer electronics (CE), seamlessly integrating into domains, such as FinTech, smart homes, autonomous driving, and information security. By processing diverse data types, such as voice, images, videos, and wireless radio-frequency signals, AI has enhanced feature extraction, prediction, and recognition, making everyday life more convenient. However, as reliance on AI systems grows, adversarial examples (ADVs) pose a growing threat, exploiting vulnerabilities in AI models with imperceptible perturbations. These deceptive attacks have significant implications for CE systems, where security lapses can result in far-reaching societal and economic consequences.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2025. Vol. 14, no 3, p. 25-26
National Category
Computer Sciences
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URN: urn:nbn:se:mau:diva-75657DOI: 10.1109/MCE.2025.3549683ISI: 001465544900005Scopus ID: 2-s2.0-105002763900OAI: oai:DiVA.org:mau-75657DiVA, id: diva2:1955246
Available from: 2025-04-29 Created: 2025-04-29 Last updated: 2025-04-29Bibliographically approved

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Malekian, Reza

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