Wild patterns: Ten years after the rise of adversarial machine learning

Battista Biggio, Fabio Roli

Abstract: 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.