8 citations · 31 across the 15 of their papers we have counts for
9 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…
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…
A Certifiable Security Patch for Object Tracking in Self-Driving Systems via Historical Deviation Modeling
Xudong Pan, Qifan Xiao, Mi Zhang +1
Self-driving cars (SDC) commonly implement the perception pipeline to detect the surrounding obstacles and track their moving trajectories, which lays the ground for the subsequent…
Cracking White-box DNN Watermarks via Invariant Neuron Transforms
Yifan Yan, Xudong Pan, Yining Wang +2
Recently, how to protect the Intellectual Property (IP) of deep neural networks (DNN) becomes a major concern for the AI industry. To combat potential model piracy, recent works ex…
MetaV: A Meta-Verifier Approach to Task-Agnostic Model Fingerprinting
Xudong Pan, Yifan Yan, Mi Zhang +1
For model piracy forensics, previous model fingerprinting schemes are commonly based on adversarial examples constructed for the owner's model as the \textit{fingerprint}, and veri…
Exploring the Security Boundary of Data Reconstruction via Neuron Exclusivity Analysis
Xudong Pan, Mi Zhang, Yifan Yan +2
Among existing privacy attacks on the gradient of neural networks, \emph{data reconstruction attack}, which reverse engineers the training batch from the gradient, poses a severe t…