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 …

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

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 …

Deep learning models and the digital records they generate have remarkably increased
their adoption of many practical applications. While the success of deep learning in …

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 …

Deep neural network (DNN) models are valuable intellectual property of model owners,
constituting a competitive advantage. Therefore, it is crucial to develop techniques to protect …

Along with the evolution of deep neural networks (DNNs) in many real-world applications,
the complexity of model building has also dramatically increased. Therefore, it is vital to …

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 …

Since the training of deep neural networks (DNN) models requires massive training data,
time and expensive hardware resources, the trained DNN model is oftentimes regarded as …