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

Diffusion models (DMs) have demonstrated advantageous potential on generative tasks.
Widespread interest exists in incorporating DMs into downstream applications, such as …

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 …

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 …

The rise of large language model-based code generation (LLCG) has enabled various
commercial services and APIs. Training LLCG models is often expensive and time …

Watermarking is a commonly used strategy to protect creators' rights to digital images,
videos and audio. Recently, watermarking methods have been extended to deep learning …

Deep neural networks (DNNs) have become state-of-the-art in many application domains.
The increasing complexity and cost for building these models demand means for protecting …

Obtaining a well-trained model involves expensive data collection and training procedures,
therefore the model is a valuable intellectual property. Recent studies revealed that …

With the advance of deep learning, it definitely has achieved the unprecedented success in
the community of artificial intelligence. However, the issue of the intellectual property (IP) …

The rapid advancement of generative models facilitating the creation of hyper-realistic
images from textual descriptions has concurrently escalated critical societal concerns such …