论文标题
一项针对监督机器学习的中毒攻击的调查
A Survey on Poisoning Attacks Against Supervised Machine Learning
论文作者
论文摘要
随着现代计算中人工智能和机器学习的兴起，有关此类技术的主要问题之一是为对手提供隐私和安全性。我们介绍这份调查文件，以涵盖对监督机器学习模型中毒攻击中最具代表性的论文。我们首先提供分类法以对现有研究进行分类，然后为选定论文提供详细的摘要。我们总结并比较了现有文献的方法和局限性。我们以潜在的改进和未来的方向来结束本文，以进一步利用并防止对监督模型的中毒攻击。我们提出了几个未解决的研究问题，以鼓励和激发研究人员未来的工作。
With the rise of artificial intelligence and machine learning in modern computing, one of the major concerns regarding such techniques is to provide privacy and security against adversaries. We present this survey paper to cover the most representative papers in poisoning attacks against supervised machine learning models. We first provide a taxonomy to categorize existing studies and then present detailed summaries for selected papers. We summarize and compare the methodology and limitations of existing literature. We conclude this paper with potential improvements and future directions to further exploit and prevent poisoning attacks on supervised models. We propose several unanswered research questions to encourage and inspire researchers for future work.
