Wild patterns: Ten years after the rise of adversarial machine learning
作者：
Highlights：
• We provide a detailed review of the evolution of adversarial machine learning over the last ten years.
• We start from pioneering work up to more recent work aimed at understanding the security properties of deep learning algorithms.
• We review work in the context of different applications.
• We highlight common misconceptions related to the evaluation of the security of machinelearning and pattern recognition algorithms.
• We discuss the main limitations of current work, along with the corresponding future research paths towards designing more secure learning algorithms.
摘要
•We provide a detailed review of the evolution of adversarial machine learning over the last ten years.•We start from pioneering work up to more recent work aimed at understanding the security properties of deep learning algorithms.•We review work in the context of different applications.•We highlight common misconceptions related to the evaluation of the security of machinelearning and pattern recognition algorithms.•We discuss the main limitations of current work, along with the corresponding future research paths towards designing more secure learning algorithms.
论文关键词：Adversarial machine learning,Evasion attacks,Poisoning attacks,Adversarial examples,Secure learning,Deep learning
论文评审过程：Received 8 December 2017, Revised 29 June 2018, Accepted 16 July 2018, Available online 21 July 2018, Version of Record 29 July 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.07.023
