50 citations · 81 across the 13 of their papers we have counts for
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cs.CR2023
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…
cs.CR2023★ 4 cited
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…
cs.CV2023
UMDFood: Vision-language models boost food composition compilation
Peihua Ma, Yixin Wu, Ning Yu +4
Nutrition information is crucial in precision nutrition and the food industry. The current food composition compilation paradigm relies on laborious and experience-dependent method…