8 citations · 31 across the 15 of their papers we have counts for
15 papers
Large language model-powered AI systems achieve self-replication with no human intervention
Xudong Pan, Jiarun Dai, Yihe Fan +3
Self-replication with no human intervention is broadly recognized as one of the principal red lines associated with frontier AI systems. While leading corporations such as OpenAI a…
Frontier AI systems have surpassed the self-replicating red line
Xudong Pan, Jiarun Dai, Yihe Fan +1
Successful self-replication under no human assistance is the essential step for AI to outsmart the human beings, and is an early signal for rogue AIs. That is why self-replication…
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…
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…
Matryoshka: Stealing Functionality of Private ML Data by Hiding Models in Model
Xudong Pan, Yifan Yan, Shengyao Zhang +2
In this paper, we present a novel insider attack called Matryoshka, which employs an irrelevant scheduled-to-publish DNN model as a carrier model for covert transmission of multipl…
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…
How Sequence-to-Sequence Models Perceive Language Styles?
Ruozi Huang, Mi Zhang, Xudong Pan +1
Style is ubiquitous in our daily language uses, while what is language style to learning machines? In this paper, by exploiting the second-order statistics of semantic vectors of d…
Theoretical Analysis of Image-to-Image Translation with Adversarial Learning
Xudong Pan, Mi Zhang, Daizong Ding
Recently, a unified model for image-to-image translation tasks within adversarial learning framework has aroused widespread research interests in computer vision practitioners. The…