134 citations · 152 across the 20 of their papers we have counts for
6 papers · 1 filter
FAKEPCD: Fake Point Cloud Detection via Source Attribution
Yiting Qu, Zhikun Zhang, Yun Shen +2
To prevent the mischievous use of synthetic (fake) point clouds produced by generative models, we pioneer the study of detecting point cloud authenticity and attributing them to th…
Comprehensive Assessment of Toxicity in ChatGPT
Boyang Zhang, Xinyue Shen, Wai Man Si +6
Moderating offensive, hateful, and toxic language has always been an important but challenging topic in the domain of safe use in NLP. The emerging large language models (LLMs), su…
Quantifying Privacy Risks of Prompts in Visual Prompt Learning
Yixin Wu, Rui Wen, Michael Backes +4
Large-scale pre-trained models are increasingly adapted to downstream tasks through a new paradigm called prompt learning. In contrast to fine-tuning, prompt learning does not upda…
Prompt Backdoors in Visual Prompt Learning
Hai Huang, Zhengyu Zhao, Michael Backes +2
Fine-tuning large pre-trained computer vision models is infeasible for resource-limited users. Visual prompt learning (VPL) has thus emerged to provide an efficient and flexible al…
On the Proactive Generation of Unsafe Images From Text-To-Image Models Using Benign Prompts
Yixin Wu, Ning Yu, Michael Backes +2
Malicious or manipulated prompts are known to exploit text-to-image models to generate unsafe images. Existing studies, however, focus on the passive exploitation of such harmful c…
Composite Backdoor Attacks Against Large Language Models
Hai Huang, Zhengyu Zhao, Michael Backes +2
Large language models (LLMs) have demonstrated superior performance compared to previous methods on various tasks, and often serve as the foundation models for many researches and…