3 citations · 5 across the 29 of their papers we have counts for
4 papers · 1 filter
New Paradigm of Adversarial Training: Releasing Accuracy-Robustness Trade-Off via Dummy Class
Yanyun Wang, Li Liu, Zi Liang +4
Adversarial Training (AT) is one of the most effective methods to enhance the robustness of Deep Neural Networks (DNNs). However, existing AT methods suffer from an inherent accura…
Beyond Perceptual Distance: Discrepancy Assessment on Deep Representation for Out-of-Distribution Detection with Diffusion Model
Kun Fang, Zuopeng Yang, Haibo Hu +3
Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection, i.e., In-Distribution (InD), or fro…
"Yes, My LoRD." Guiding Language Model Extraction with Locality Reinforced Distillation
Zi Liang, Qingqing Ye, Yanyun Wang +5
Model extraction attacks (MEAs) on large language models (LLMs) have received increasing attention in recent research. However, existing attack methods typically adapt the extracti…
Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models
Zi Liang, Haibo Hu, Qingqing Ye +2
The drastic increase of large language models' (LLMs) parameters has led to a new research direction of fine-tuning-free downstream customization by prompts, i.e., task description…