3 citations · 4 across the 5 of their papers we have counts for
7 papers · 1 filter
DIPBox: A Multi-scale Testing Framework for Tracking Dataset Regeneration
Tian Dong, Yan Meng, Shaofeng Li +5
Training datasets have tremendous proprietary value and are vulnerable to unauthorized copying. Existing defenses mainly focus on tracking individual data points, but pay little at…
Hunting Vulnerability Variants in AI Infra: Measurement and Reference-Driven Detection
Tian Dong, Yanjun Chen, Shoufeng Zhang +6
AI infra has become a shared execution layer for model training, deployment, and agent orchestration. Because many projects reimplement similar model-centric workflows, a vulnerabi…
Depth Gives a False Sense of Privacy: LLM Internal States Inversion
Tian Dong, Yan Meng, Shaofeng Li +3
Large Language Models (LLMs) are increasingly integrated into daily routines, yet they raise significant privacy and safety concerns. Recent research proposes collaborative inferen…
The Philosopher's Stone: Trojaning Plugins of Large Language Models
Tian Dong, Minhui Xue, Guoxing Chen +5
Open-source Large Language Models (LLMs) have recently gained popularity because of their comparable performance to proprietary LLMs. To efficiently fulfill domain-specialized task…
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations
Zirui Peng, Shaofeng Li, Guoxing Chen +3
In this paper, we propose a novel and practical mechanism which enables the service provider to verify whether a suspect model is stolen from the victim model via model extraction…
Deep Learning Backdoors
Shaofeng Li, Shiqing Ma, Minhui Xue +1
Intuitively, a backdoor attack against Deep Neural Networks (DNNs) is to inject hidden malicious behaviors into DNNs such that the backdoor model behaves legitimately for benign in…