Smarter applications are making better use of the insights gleaned from data, having an
impact on every industry and research discipline. At the core of this revolution lies the tools …

The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

As a research community, we are still lacking a systematic understanding of the progress on
adversarial robustness which often makes it hard to identify the most promising ideas in …

While machine learning (ML) models are being increasingly trusted to make decisions in
different and varying areas, the safety of systems using such models has become an …

Backdoor learning is an emerging and vital topic for studying deep neural networks'
vulnerability (DNNs). Many pioneering backdoor attack and defense methods are being …

Accuracy is an important concern for suppliers of artificial intelligence (AI) services, but
considerations beyond accuracy, such as safety (which includes fairness and explainability) …

The success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …

The robustness of deep neural networks is frequently compromised when faced with
adversarial examples, common corruptions, and distribution shifts, posing a significant …

Deep neural networks are vulnerable to adversarial examples, which becomes one of the
most important research problems in the development of deep learning. While a lot of efforts …

In reaction to concerns about a broad range of potential ethical issues, dozens of proposals
for addressing ethical aspects of artificial intelligence (AI) have been published. However …