Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

The prevalence of deep learning has drawn attention to the privacy protection of sensitive
data. Various privacy threats have been presented, where an adversary can steal model …

Artificial Intelligence (AI) relies heavily on deep learning-a technology that is becoming
increasingly popular in real-life applications of AI, even in the safety-critical and high-risk …

In this paper, we propose a novel and practical mechanism which enables the service
provider to verify whether a suspect model is stolen from the victim model via model …

Deep neural networks (DNNs) have become state-of-the-art in many application domains.
The increasing complexity and cost for building these models demand means for protecting …

Benefiting from the advance of deep learning (DL) technology, Internet-of-Things (IoT)
devices and systems are becoming more intelligent and multifunctional. They are expected …

An off-the-shelf model as a commercial service could be stolen by model stealing attacks,
posing great threats to the rights of the model owner. Model fingerprinting aims to verify …

As mobile devices are becoming ubiquitous, regularly interacting with a variety of user
interfaces (UIs) is a common aspect of daily life for many people. To improve the …

Neural networks have become increasingly prevalent in many real-world applications
including security critical ones. Due to the high hardware requirement and time consumption …

Deep Neural Networks (DNNs), from AlexNet to ResNet to ChatGPT, have made
revolutionary progress in recent years, and are widely used in various fields. The high …