A learned system uses machine learning (ML) internally to improve performance. We can
expect such systems to be vulnerable to some adversarial-ML attacks. Often, the learned …

Machine learning (ML) models deployed in many safety-and business-critical systems are
vulnerable to exploitation through adversarial examples. A large body of academic research …

Many of today's machine learning (ML) systems are built by reusing an array of, often pre-
trained, primitive models, each fulfilling distinct functionality (eg, feature extraction). The …

Recent research1 has shown that machine learning models are venerable to attacks by
adversaries almost at all phases of machine learning pipeline, such as positioning attacks …

While machine learning is vulnerable to adversarial examples, it still lacks systematic
procedures and tools for evaluating its security in different contexts. We discuss how to …

Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as data analytics, autonomous systems, and security diagnostics. ML is …

Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as data analytics, autonomous systems, and security diagnostics. ML is …

Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as object recognition, autonomous systems, security diagnostics, and …

Machine learning is a field of artificial intelligence (AI) that is becoming essential for several
critical systems, making it a good target for threat actors. Threat actors exploit different …

Machine learning has become prevalent in transforming diverse aspects of our daily lives
through intelligent digital solutions. Advanced disease diagnosis, autonomous vehicular …