Adversarial Machine Learning for Robust Prediction

With continued advances in science and technology, digital data have grown at an astonishing rate in various domains and forms, such as business, geography, health, multimedia, network, text, and web data. Machine learning, a powerful tool for automatically extracting, managing, inferencing, and transferring knowledge, has been proven to be extremely useful in understanding the intrinsic nature of real-world big data. Despite achieving remarkable performance, machine learning models, especially deep learning models, suffer from harassment caused by small adversarial perturbations injected by malicious parties and users. There is an immediate and crucial need for theoretical and practical techniques to identify the vulnerability of machine learning models and explore the defense mechanism and the certifiable robustness.The goal of this Research Topic is to present state-of-the-art methodologies build upon an innovative blend of techniques from computer science, mathematics, and statistics, and to greatly expand the reach of adversarial machine learning from both theoretical and practical points of view, allowing the machine learning models to be deployed in safety and security-critical applications. This Research Topic will focus on three main research tasks: (1) How to develop effective modification 'attack' strategies to tamper with intrinsic characteristics of data by injecting fake information? (2) How to develop defense strategies to offer sufficient protection to mach...