Federated learning (FL) is a technique that allows multiple participants to collaboratively
train a Deep Neural Network (DNN) without the need to centralize their data. Among other …

Federated Learning is a collaborative machine learning paradigm that allows training
models on decentralized data while preserving data privacy. It has gained significant …

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 …

Federated learning, known for its emphasis on privacy and resource efficiency, has emerged
as a transformative paradigm in the fields of artificial intelligence and industrial machine …

Ownership verification is currently the most critical and widely adopted post-hoc method to
safeguard model copyright. In general, model owners exploit it to identify whether a given …

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 …

Recently, numerous highly-valuable Deep Neural Networks (DNNs) have been trained
using deep learning algorithms. To protect the Intellectual Property (IP) of the original …

With deep learning techniques achieving great results in modern industry, the intellectual
property (IP) protection for deep learning models has attracted the attention of academics …

In the realm of edge AI systems where deep learning is paramount, protecting the
intellectual property (IP) of multimodal neural network models is crucial. Current …

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 …