6 citations · 11 across the 14 of their papers we have counts for
14 papers
What You See Is Not What AI Gets: DPAgent-in-the-Middle Defense Against AI-Groomed Deceptive Patterns
Zewei Shi, Ruoxi Sun, Haoyang Li +4
Privacy deceptive patterns in web interfaces manipulate users into disclosing personal data, yet existing defenses are fragmented, static, and increasingly vulnerable to manipulati…
If you're waiting for a sign... that might not be it! Mitigating Trust Boundary Confusion from Visual Injections on Vision-Language Agentic Systems
Jiamin Chang, Minhui Xue, Ruoxi Sun +3
Recent advances in embodied Vision-Language Agentic Systems (VLAS), powered by large vision-language models (LVLMs), enable AI systems to perceive and reason over real-world scenes…
Keep the Lights On, Keep the Lengths in Check: Plug-In Adversarial Detection for Time-Series LLMs in Energy Forecasting
Hua Ma, Ruoxi Sun, Minhui Xue +4
Accurate time-series forecasting is increasingly critical for planning and operations in low-carbon power systems. Emerging time-series large language models (TS-LLMs) now deliver…
Re-Key-Free, Risky-Free: Adaptable Model Usage Control
Zihan Wang, Zhongkui Ma, Xinguo Feng +6
Deep neural networks (DNNs) have become valuable intellectual property of model owners, due to the substantial resources required for their development. To protect these assets in…
A Meta-Analysis of LLM Effects on Students across Qualification, Socialisation, and Subjectification
Jiayu Huang, Ruoxin Ritter Wang, Jen-Hao Liu +5
Large language models (LLMs) are increasingly positioned as solutions for education, yet evaluations often reduce their impact to narrow performance metrics. This paper reframes th…
What's Pulling the Strings? Evaluating Integrity and Attribution in AI Training and Inference through Concept Shift
Jiamin Chang, Haoyang Li, Hammond Pearce +3
The growing adoption of artificial intelligence (AI) has amplified concerns about trustworthiness, including integrity, privacy, robustness, and bias. To assess and attribute these…
50 Shades of Deceptive Patterns: A Unified Taxonomy, Multimodal Detection, and Security Implications
Zewei Shi, Ruoxi Sun, Jieshan Chen +5
Deceptive patterns (DPs) are user interface designs deliberately crafted to manipulate users into unintended decisions, often by exploiting cognitive biases for the benefit of comp…
SoK: The Security-Safety Continuum of Multimodal Foundation Models through Information Flow and Global Game-Theoretic Analysis of Asymmetric Threats
Ruoxi Sun, Jiamin Chang, Hammond Pearce +5
Multimodal foundation models (MFMs) integrate diverse data modalities to support complex and wide-ranging tasks. However, this integration also introduces distinct safety and secur…
Edge Unlearning is Not "on Edge"! An Adaptive Exact Unlearning System on Resource-Constrained Devices
Xiaoyu Xia, Ziqi Wang, Ruoxi Sun +3
The right to be forgotten mandates that machine learning models enable the erasure of a data owner's data and information from a trained model. Removing data from the dataset alone…
The Invisible Game on the Internet: A Case Study of Decoding Deceptive Patterns
Zewei Shi, Ruoxi Sun, Jieshan Chen +2
Deceptive patterns are design practices embedded in digital platforms to manipulate users, representing a widespread and long-standing issue in the web and mobile software developm…
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…
Bounded and Unbiased Composite Differential Privacy
Kai Zhang, Yanjun Zhang, Ruoxi Sun +5
The objective of differential privacy (DP) is to protect privacy by producing an output distribution that is indistinguishable between any two neighboring databases. However, tradi…
RAI4IoE: Responsible AI for Enabling the Internet of Energy
Minhui Xue, Surya Nepal, Ling Liu +5
This paper plans to develop an Equitable and Responsible AI framework with enabling techniques and algorithms for the Internet of Energy (IoE), in short, RAI4IoE. The energy sector…
A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services
Hongsheng Hu, Shuo Wang, Jiamin Chang +5
The right to be forgotten requires the removal or "unlearning" of a user's data from machine learning models. However, in the context of Machine Learning as a Service (MLaaS), retr…