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 …

In many industrial applications, datasets are often obtained in a sequence associated with a
series of similar but different tasks. To model these datasets, a machine‐learning algorithm …

Abstract Vision Transformers (ViTs) have a radically different architecture with significantly
less inductive bias than Convolutional Neural Networks. Along with the improvement in …

Federated learning (FL) is a distributed machine learning paradigm allowing multiple clients
to collaboratively train a global model without sharing their local data. However, FL entails …

Watermarking has been widely adopted for protecting the intellectual property (IP) of Deep
Neural Networks (DNN) to defend the unauthorized distribution. Unfortunately, studies have …

We propose a novel multi-bit box-free watermarking method for the protection of Intellectual
Property Rights (IPR) of GANs with improved robustness against white-box model-level …

Given a well-behaved neural network is possible to identify its parent based on which it was
tuned? In this paper we introduce a novel task known as neural lineage detection aiming at …

Tree models are very widely used in practice of machine learning and data mining. In this
paper, we study the problem of model integrity authentication in tree models. In general, the …

The recent privacy leakage incidences and the more strict policy regulations demand a
much higher standard of compliance for companies and mobile apps. However, such …

With the growing applications of Deep Learning (DL), especially recent spectacular
achievements of Large Language Models (LLMs) such as ChatGPT and LLaMA, the …