With the rapid growth of interest in and use of large language models (LLMs) across various
industries, we are facing some crucial and profound ethical concerns, especially in the …

Generative image modeling enables a wide range of applications but raises ethical
concerns about responsible deployment. This paper introduces an active strategy combining …

We present REMARK-LLM, a novel efficient, and robust watermarking framework designed
for texts generated by large language models (LLMs). Synthesizing human-like content …

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 …

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 …

Watermarking has been widely adopted for protecting the intellectual property (IP) of Deep
Neural Networks (DNN) to defend the unauthorized distribution. Unfortunately, studies have …

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 …

The rise of generative neural networks has triggered an increased demand for intellectual
property (IP) protection in generated content. Deep watermarking techniques, recognized for …

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

Recent years have witnessed significant progress in developing deep learning-based
models for automated code completion. Although using source code in GitHub has been a …