首篇系统综述表格数据对抗攻击，梳理方法与挑战。
Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review
- 首次系统整理表格数据对抗攻击方法
- 归纳攻击策略并分析实际应用可行性
- 适合关注数据安全的机器学习研究者
机器学习中的对抗攻击在计算机视觉和自然语言处理领域已有广泛综述，但针对表格数据的研究仍零散。本文首次对面向表格机器学习模型的对抗攻击进行系统性文献回顾。我们揭示了主要研究趋势，对攻击策略进行分类，并分析其如何应对真实应用场景的实际考量。此外，还指出了当前面临的挑战与开放性问题。通过提供清晰、结构化的概览，本综述旨在引导未来研究深入理解并应对表格机器学习中的对抗脆弱性。
原文摘要 · Abstract (English)
Adversarial attacks in machine learning have been extensively reviewed in areas like computer vision and NLP, but research on tabular data remains scattered. This paper provides the first systematic literature review focused on adversarial attacks targeting tabular machine learning models. We highlight key trends, categorize attack strategies and analyze how they address practical considerations for real-world applicability. Additionally, we outline current challenges and open research questions. By offering a clear and structured overview, this review aims to guide future efforts in understanding and addressing adversarial vulnerabilities in tabular machine learning.
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