151 citations · 413 across the 25 of their papers we have counts for
5 papers · 1 filter
Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion
Guanchu Wang, Yu-Neng Chuang, Ruixiang Tang +8
Ensuring the security of released large language models (LLMs) poses a significant dilemma, as existing mechanisms either compromise ownership rights or raise data privacy concerns…
LoRATK: LoRA Once, Backdoor Everywhere in the Share-and-Play Ecosystem
Hongyi Liu, Shaochen Zhong, Xintong Sun +12
Finetuning LLMs with LoRA has gained significant popularity due to its simplicity and effectiveness. Often, users may even find pluggable, community-shared LoRAs to enhance their b…
Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking
Ruixiang Tang, Qizhang Feng, Ninghao Liu +2
The huge supporting training data on the Internet has been a key factor in the success of deep learning models. However, this abundance of public-available data also raises concern…
Deep Serial Number: Computational Watermarking for DNN Intellectual Property Protection
Ruixiang Tang, Mengnan Du, Xia Hu
In this paper, we present DSN (Deep Serial Number), a simple yet effective watermarking algorithm designed specifically for deep neural networks (DNNs). Unlike traditional methods…
An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks
Ruixiang Tang, Mengnan Du, Ninghao Liu +2
With the widespread use of deep neural networks (DNNs) in high-stake applications, the security problem of the DNN models has received extensive attention. In this paper, we invest…