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

With the widespread use of large artificial intelligence (AI) models such as ChatGPT, AI-
generated content (AIGC) has garnered increasing attention and is leading a paradigm shift …

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 …

Watermarking neural networks is a quite important means to protect the intellectual property
(IP) of neural networks. In this paper, we introduce a novel digital watermarking framework …

Despite the tremendous success, deep neural networks are exposed to serious IP
infringement risks. Given a target deep model, if the attacker knows its full information, it can …

Recent advances in natural language generation have introduced powerful language
models with high-quality output text. However, this raises concerns about the potential …

Deep learning models, especially those large-scale and high-performance ones, can be
very costly to train, demanding a considerable amount of data and computational resources …

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

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 …