Learning-based pattern classifiers, including deep networks, have demonstrated impressive performance in several application domains, ranging from computer vision to computer security. However, it has also been shown that adversarial input perturbations carefully crafted either at training or at test time can easily subvert their predictions. The vulnerability of machine learning to adversarial inputs (also known as adversarial examples), along with the design of suitable countermeasures, have been investigated in the research field of adversarial machine learning. In this work, we provide a thorough overview of the evolution of this interdisciplinary research area over the last ten years, starting from pioneering, earlier work up to more recent work aimed at understanding the security properties of deep learning algorithms, in the context of different applications. We report interesting connections between these apparently-different lines of work, highlighting common misconceptions related to the evaluation of the security of machine-learning algorithms. We finally discuss the main limitations of current work, along with the corresponding future research challenges towards the design of more secure learning algorithms. Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning