AI RESEARCH
Comparative Insights on Adversarial Machine Learning from Industry and Academia: A User-Study Approach
arXiv CS.AI
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ArXi:2602.04753v2 Announce Type: replace-cross An exponential growth of Machine Learning and its Generative AI applications brings with it significant security challenges, often referred to as Adversarial Machine Learning (AML). In this paper, we conducted two comprehensive studies to explore the perspectives of industry professionals and students on different AML vulnerabilities and their educational strategies. In our first study, we conducted an online survey with professionals revealing a notable correlation between cybersecurity education and concern for AML threats.