Machine learning (ML) models are applied in an increasing variety of domains. The
availability of large amounts of data and computational resources encourages the …

Abstract Protecting the Intellectual Property Rights (IPR) associated to Deep Neural
Networks (DNNs) is a pressing need pushed by the high costs required to train such …

AI has undergone a remarkable evolution journey marked by groundbreaking milestones.
Like any powerful tool, it can be turned into a weapon for devastation in the wrong hands …

Protecting the copyright of large language models (LLMs) has become crucial due to their
resource-intensive training and accompanying carefully designed licenses. However …

The training and creation of deep learning model is usually costly, thus the trained model
can be regarded as an intellectual property (IP) of the model creator. However, malicious …

The commercial use of machine learning (ML) is spreading; at the same time, ML models
are becoming more complex and more expensive to train, which makes intellectual property …

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 …

Abstract In recent decades, Generative Adversarial Network (GAN) and its variants have
achieved unprecedented success in image synthesis. However, well-trained GANs are …

The unprecedented success of deep learning could not be achieved without the synergy of
big data, computing power, and human knowledge, among which none is free. This calls for …

Federated learning (FL), an effective distributed machine learning framework, implements
model training and meanwhile protects local data privacy. It has been applied to a broad …