Machine Learning (ML) algorithms, specifically supervised learning, are widely used in
modern real-world applications, which utilize Computational Intelligence (CI) as their core …

Adversarial phenomenon has been widely observed in machine learning (ML) systems,
especially in those using deep neural networks, describing that ML systems may produce …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

Adversarial Robustness for Machine Learning summarizes the recent progress on this topic
and introduces popular algorithms on adversarial attack, defense and veri? cation. Sections …

Evaluating robustness of machine-learning models to adversarial examples is a challenging
problem. Many defenses have been shown to provide a false sense of robustness by …

Adversarial robustness studies the worst-case performance of a machine learning model to
ensure safety and reliability. With the proliferation of deep-learning-based technology, the …

Cyber-security is the practice of protecting computing systems and networks from digital
attacks, which are a rising concern in the Information Age. With the growing pace at which …

Machine learning systems have had enormous success in a wide range of fields from
computer vision, natural language processing, and anomaly detection. However, such …

Deep learning has revolutionized computer vision with phenomenal success and
widespread applications. Despite impressive results in complex problems, neural networks …

Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by
imperceptible perturbations. A range of defense techniques have been proposed to improve …