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12 April 2021 Recent advances in adversarial machine learning: status, challenges and perspectives
Atul Rawal, Danda Rawat, Brian M. Sadler
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Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117462Q (2021) https://doi.org/10.1117/12.2583970
Event: SPIE Defense + Commercial Sensing, 2021, Online Only
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Abstract
The recent advances in machine learning (ML) and Artificial Intelligence (AI) have resulted in widespread application of data-driven learning algorithms. Rapid growth of AI/ML and their penetration within a plethora of civilian and military applications, while successful, has also opened new vulnerabilities. It is now clear that ML algorithms for AI systems are viable targets for malicious attacks. Therefore, there is a pressing need for better understanding of adversarial attacks against ML models, in order to secure them against such malicious attacks. In this paper, we present a survey of adversarial machine learning and some associated countermeasures. We also present a taxonomy of ML/AI system attacks that follow the same properties and characteristics, allowing them to be linked with different defensive approaches. A taxonomy is given for both attack and defense, and attacks proposed in the literature are categorized according to our taxonomy.
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Atul Rawal, Danda Rawat, and Brian M. Sadler "Recent advances in adversarial machine learning: status, challenges and perspectives", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117462Q (12 April 2021); https://doi.org/10.1117/12.2583970
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KEYWORDS
Artificial intelligence
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