Eugene Vorobeychik: Adversarial Machine Learning: from Models to Practice

Adversarial Machine Learning: from Models to Practice Machine learning (ML) techniques are increasingly used in a broad array of high-stakes applications, including cybersecurity and autonomous driving. However, ML models are often susceptible to adversarial example attacks, in which an adversary makes changes to the input in order to cause misclassification; for example, an adversary may modify malware in order for it to bypass ML-based malware detectors. A conventional approach to evaluate ML robustness