Foundation models (FM) have demonstrated remarkable performance across a wide range
of tasks (especially in the fields of natural language processing and computer vision) …

The advent of the Internet of Things (IoT) has brought forth an era of unprecedented
connectivity, with an estimated 80 billion smart devices expected to be in operation by the …

Large language models (LLMs) have witnessed a meteoric rise in popularity among the
general public users over the past few months, facilitating diverse downstream tasks with …

Large language models (LLMs) have strong capabilities in solving diverse natural language
processing tasks. However, the safety and security issues of LLM systems have become the …

Explainable AI encourages machine learning applications in the real world, whereas data-
free model extraction attacks (DFME), in which an adversary steals a trained machine …

Due to the excellent efficiency and high interpretability, coupled with the accuracy
comparable to deep learning on tabular data, various tree-based models have been widely …

As a promising service, Machine Learning as a Service (MLaaS) provides personalized
inference functions for clients through paid APIs. Nevertheless, it is vulnerable to model …

Malicious utilization of a query interface can compromise the confidentiality of ML-as-a-
Service (MLaaS) systems via model extraction attacks. Previous studies have proposed to …

With the escalating complexity and investment cost of training deep neural networks
safeguarding them from unauthorized usage and intellectual property theft has become …

Deep learning has been used to address various problems in a range of domains within
both academia and industry. However, the issue of intellectual property with deep learning …