4 citations · 4 across the 3 of their papers we have counts for
5 papers
Differentially Private Natural Gradient Descent
Pan Li, Kai Chen, Shuai Chang +3
Under a fixed privacy budget, the utility of differentially private (DP) training is ultimately determined by its optimization efficiency. Standard first-order DP optimizers such a…
LoRAGuard: An Effective Black-box Watermarking Approach for LoRAs
Peizhuo Lv, Yiran Xiahou, Congyi Li +4
LoRA (Low-Rank Adaptation) has achieved remarkable success in the parameter-efficient fine-tuning of large models. The trained LoRA matrix can be integrated with the base model thr…
HufuNet: Embedding the Left Piece as Watermark and Keeping the Right Piece for Ownership Verification in Deep Neural Networks
Peizhuo Lv, Pan Li, Shengzhi Zhang +4
Due to the wide use of highly-valuable and large-scale deep neural networks (DNNs), it becomes crucial to protect the intellectual property of DNNs so that the ownership of dispute…
SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems
Yuxuan Chen, Jiangshan Zhang, Xuejing Yuan +4
With the wide use of Automatic Speech Recognition (ASR) in applications such as human machine interaction, simultaneous interpretation, audio transcription, etc., its security prot…
Seeing isn't Believing: Practical Adversarial Attack Against Object Detectors
Yue Zhao, Hong Zhu, Ruigang Liang +3
In this paper, we presented systematic solutions to build robust and practical AEs against real world object detectors. Particularly, for Hiding Attack (HA), we proposed the featur…