Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning

Learning-based pattern classifiers, including deep networks, have shown impressive performance in several application domains, ranging from computer vision to cybersecurity. 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 such wild patterns (also referred to 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 research area over the last ten years and beyond, starting from pioneering, earlier work on the security of non-deep learning algorithms up to more recent work aimed to understand the security properties of deep learning algorithms, in the context of computer vision and cybersecurity tasks. We report interesting connections between these apparently-different lines of work, highlighting common misconceptions related to the security evaluation of machine-learning algorithms. We review the main threat models and attacks defined to this end, and discuss the main limitations of current work, along with the corresponding future challenges towards the design of more secure learning algorithms.Comment: Accepted for publication on Pattern Recognition, 201

A lattice representational definition of a hierarchy of instructional processors usable in educational courseware

The basic “recognize-act-recognize-end” cycle can be recognized in elementary as well as in more advanced forms of CAI. This article attempts to offer a unifying formal framework in which different elaborations of this cycle (embodied in a “processor”) can be placed. Three different levels of elaboration are distinguished which can be considered to be situated into the nodes of a lattice of models of the instructional process. A formal definition of such a framework can serve at least two functions. In the first place a uniform and precise definition of various elaborations can be given and new elaborations can be created in a logically funded way. Secondly, such a framework can support the modelling of instructional processes and the stimulation of student behavior. Thus, pre-testing of courseware could become feasible. Aspects of the framework have been used to implement two prototypes of support systems for the development of CAI courseware