In recent years, artificial intelligence technologies have been widely used in computer
vision, natural language processing, automatic driving, and other fields. However, artificial …

The trustworthiness of machine learning has emerged as a critical topic in the field,
encompassing various applications and research areas such as robustness, security …

This paper investigates strategies that defend against adversarial-example attacks on image-
classification systems by transforming the inputs before feeding them to the system …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

While deep learning has shown a great potential in various domains, the lack of
transparency has limited its application in security or safety-critical areas. Existing research …

Deployed multimodal models can fail in ways that evaluators did not anticipate. In order to
find these failures before deployment, we introduce MultiMon, a system that automatically …

Recently, many studies have demonstrated deep neural network (DNN) classifiers can be
fooled by the adversarial example, which is crafted via introducing some perturbations into …

In practice, deep neural networks have been found to be vulnerable to various types of
noise, such as adversarial examples and corruption. Various adversarial defense methods …

While deep neural networks have been achieving state-of-the-art performance across a
wide variety of applications, their vulnerability to adversarial attacks limits their widespread …

Deep learning has presented remarkable progress in various tasks. Despite the excellent
performance, deep learning models remain not robust, especially to well-designed …