1 citations · 2 across the 20 of their papers we have counts for
22 papers
TransMeme: A Multi-Agent Framework for Cross-Cultural Meme Transcreation
Jingyi Zheng, Yule Liu, Zifan Peng +4
Internet memes are a pervasive form of multimodal online communication; however, such communication often involves users from diverse linguistic and cultural backgrounds. Therefore…
What Your Posts Reveal: A Benchmark and Agentic Framework for User-Level Privacy Leakage on Social Media
Zifan Peng, Yini Huang, Aiwen Lu +7
Public social media posts can reveal private information through weak cues scattered across text, images, or metadata. Such leakage is often cumulative and cross-post: cues that ap…
Beyond Waveform Robustness: Robust Feature-Vocoder Adversarial Attacks on Automatic Speech Recognition
Yifan Liao, Zongmin Zhang, Zhen Sun +3
Automatic speech recognition (ASR) systems have become widely used for multilingual speech-to-text transcription. Their robustness to adversarial attacks has become an important to…
When New Generators Arrive: Lifelong Machine-Generated Text Attribution via Ridge Feature Transfer
Zhen Sun, Yifan Liao, Zhicong Huang +4
Machine-generated text (MGT) attribution aims to identify the specific generator responsible for a given text, thereby providing fine-grained evidence for model accountability and…
CHASM: Unveiling Covert Advertisements on Chinese Social Media
Jingyi Zheng, Tianyi Hu, Yule Liu +5
Current benchmarks for evaluating large language models (LLMs) in social media moderation completely overlook a serious threat: covert advertisements, which disguise themselves as…
6DAttack: Backdoor Attacks in the 6DoF Pose Estimation
Jihui Guo, Zongmin Zhang, Zhen Sun +4
Deep learning advances have enabled accurate six-degree-of-freedom (6DoF) object pose estimation, widely used in robotics, AR/VR, and autonomous systems. However, backdoor attacks…
"To Survive, I Must Defect": Jailbreaking LLMs via the Game-Theory Scenarios
Zhen Sun, Zongmin Zhang, Deqi Liang +8
As LLMs become more common, non-expert users can pose risks, prompting extensive research into jailbreak attacks. However, most existing black-box jailbreak attacks rely on hand-cr…
Auditing Data Membership in Reinforcement Learning With Verifiable Rewards
Yule Liu, Heyi Zhang, Jinyi Zheng +6
Reinforcement Learning with Verifiable Rewards (RLVR) has become a core training stage in recent large language models (LLMs). Its reliance on non-public, high-value prompt sets ra…
ZPD-SCA: Unveiling the Blind Spots of LLMs in Assessing Students' Cognitive Abilities
Wenhan Dong, Zhen Sun, Yuemeng Zhao +9
Large language models (LLMs) have demonstrated potential in educational applications, yet their capacity to accurately assess the cognitive alignment of reading materials with stud…
GasAgent: A Multi-Agent Framework for Automated Gas Optimization in Smart Contracts
Jingyi Zheng, Zifan Peng, Yule Liu +4
Smart contracts are trustworthy, immutable, and automatically executed programs on the blockchain. Their execution requires the Gas mechanism to ensure efficiency and fairness. How…
Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation
Zhiyuan Zhong, Zhen Sun, Yepang Liu +2
Vision Language Models (VLMs) have shown remarkable performance, but are also vulnerable to backdoor attacks whereby the adversary can manipulate the model's outputs through hidden…
Evaluation Hallucination in Multi-Round Incomplete Information Lateral-Driven Reasoning Tasks
Wenhan Dong, Tianyi Hu, Jingyi Zheng +5
Multi-round incomplete information tasks are crucial for evaluating the lateral thinking capabilities of large language models (LLMs). Currently, research primarily relies on multi…
JALMBench: Benchmarking Jailbreak Vulnerabilities in Audio Language Models
Zifan Peng, Yule Liu, Zhen Sun +9
Large Audio Language Models (LALMs) have made significant progress. While increasingly deployed in real-world applications, LALMs face growing safety risks from jailbreak attacks t…
"I Can See Forever!": Evaluating Real-time VideoLLMs for Assisting Individuals with Visual Impairments
Ziyi Zhang, Zhen Sun, Zongmin Zhang +6
The visually impaired population faces significant challenges in daily activities. While prior works employ vision language models for assistance, most focus on static content and…
Humanizing LLMs: A Survey of Psychological Measurements with Tools, Datasets, and Human-Agent Applications
Wenhan Dong, Yuemeng Zhao, Zhen Sun +10
As large language models (LLMs) are increasingly used in human-centered tasks, assessing their psychological traits is crucial for understanding their social impact and ensuring tr…
Thought Manipulation: External Thought Can Be Efficient for Large Reasoning Models
Yule Liu, Jingyi Zheng, Zhen Sun +6
Recent advancements in large reasoning models (LRMs) have demonstrated the effectiveness of scaling test-time computation to enhance reasoning capabilities on various tasks. Howeve…
TH-Bench: Evaluating Evading Attacks via Humanizing AI Text on Machine-Generated Text Detectors
Jingyi Zheng, Junfeng Wang, Zhen Sun +3
As Large Language Models (LLMs) advance, Machine-Generated Texts (MGTs) have become increasingly fluent, high-quality, and informative. Existing wide-range MGT detectors are design…
SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
Heyi Zhang, Yule Liu, Xinlei He +3
Federated learning (FL) enables collaborative model training while preserving data privacy, but its decentralized nature exposes it to client-side data poisoning attacks (DPAs) and…
FC-Attack: Jailbreaking Multimodal Large Language Models via Auto-Generated Flowcharts
Ziyi Zhang, Zhen Sun, Zongmin Zhang +2
Multimodal Large Language Models (MLLMs) have become powerful and widely adopted in some practical applications. However, recent research has revealed their vulnerability to multim…
Unsafe LLM-Based Search: Quantitative Analysis and Mitigation of Safety Risks in AI Web Search
Zeren Luo, Zifan Peng, Yule Liu +4
Recent advancements in Large Language Models (LLMs) have significantly enhanced the capabilities of AI-Powered Search Engines (AIPSEs), offering precise and efficient responses by…
Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks
Wenhan Dong, Chao Lin, Xinlei He +2
Privacy-preserving federated learning (PPFL) aims to train a global model for multiple clients while maintaining their data privacy. However, current PPFL protocols exhibit one or…
Quantized Delta Weight Is Safety Keeper
Yule Liu, Zhen Sun, Xinlei He +1
Recent advancements in fine-tuning proprietary language models enable customized applications across various domains but also introduce two major challenges: high resource demands…