8 citations · 18 across the 11 of their papers we have counts for
6 papers · 1 filter
No-Skim: Towards Efficiency Robustness Evaluation on Skimming-based Language Models
Shengyao Zhang, Mi Zhang, Xudong Pan +1
To reduce the computation cost and the energy consumption in large language models (LLM), skimming-based acceleration dynamically drops unimportant tokens of the input sequence pro…
JADE: A Linguistics-based Safety Evaluation Platform for Large Language Models
Mi Zhang, Xudong Pan, Min Yang
In this paper, we present JADE, a targeted linguistic fuzzing platform which strengthens the linguistic complexity of seed questions to simultaneously and consistently break a wide…
BELT: Old-School Backdoor Attacks can Evade the State-of-the-Art Defense with Backdoor Exclusivity Lifting
Huming Qiu, Junjie Sun, Mi Zhang +2
Deep neural networks (DNNs) are susceptible to backdoor attacks, where malicious functionality is embedded to allow attackers to trigger incorrect classifications. Old-school backd…
Neural Dehydration: Effective Erasure of Black-box Watermarks from DNNs with Limited Data
Yifan Lu, Wenxuan Li, Mi Zhang +2
To protect the intellectual property of well-trained deep neural networks (DNNs), black-box watermarks, which are embedded into the prediction behavior of DNN models on a set of sp…
Rethinking White-Box Watermarks on Deep Learning Models under Neural Structural Obfuscation
Yifan Yan, Xudong Pan, Mi Zhang +1
Copyright protection for deep neural networks (DNNs) is an urgent need for AI corporations. To trace illegally distributed model copies, DNN watermarking is an emerging technique f…
Exorcising ''Wraith'': Protecting LiDAR-based Object Detector in Automated Driving System from Appearing Attacks
Qifan Xiao, Xudong Pan, Yifan Lu +3
Automated driving systems rely on 3D object detectors to recognize possible obstacles from LiDAR point clouds. However, recent works show the adversary can forge non-existent cars…