6 citations · 7 across the 10 of their papers we have counts for
13 papers
When Charts Deceive Web Agents: A Paired Benchmark for Misleading Visualization-Grounded Tasks
Yisheng Li, Dongming Han, Hongkun Pan +5
Web agents increasingly browse, interpret, and act on web content on behalf of users. In many realistic workflows, however, the visual evidence they consume is not neutral: charts…
WanPE: Towards Cinematic Prompt Enhancement for Modern Text-to-Video Generation
Yubo Zhu, Yawen Shao, Ziyun Dai +27
Video generation begins in text space by authoring a cinematic screenplay, then materializes into pixels. As contemporary video generators scale to 30 seconds and faithfully follow…
VIVAS: Vitalizing Visual Perception in VLM Pre-training via Vision-language Unified Autoregressive Supervision
Zhehan Kan, Yubo Zhu, Xinghua Jiang +10
While Vision-Language Models (VLMs) demonstrate strong capabilities, they continue to suffer from a critical limitation: insufficient fine-grained visual perception, which fundamen…
When Does Visual Generation Help Visual Understanding in Unified Multimodal Models?
Yubo Zhu, Zhehan Kan, Jingyi Yang +6
Unified multimodal models (UMMs) can perform both understanding and generation, raising a central question: can visual generation improve understanding? Existing evaluations provid…
Mitigating Database Leakage in RAG Systems with Keyword-Grounded Fact Substitution
Ziliang Zhang, Yubo Zhu, Wei Tong +4
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for combining large language models (LLMs) with external knowledge sources. However, RAG systems remain vuln…
PURPOSE: Poisoning Conflict Resolution in RAG via Proxy-Fact-Grounded Updates
Zijian Wang, Yubo Zhu, Muzhi Dong +7
In Retrieval-Augmented Generation (RAG), post-retrieval conflict resolution arbitrates among noisy or contradictory retrieved passages. However, the robustness of this safeguard ag…
Auditing Data Provenance in LLM Fine-tuning via Intrinsic Distributional Fingerprints
Zirui Huang, Yunlong Mao, Wei Tong +3
The proliferation of customized Large Language Models (LLMs) poses critical risks of Data Intellectual Property (Data IP) infringement via unauthorized fine-tuning on proprietary d…
The LLM Already Knows: Estimating LLM-Perceived Question Difficulty via Hidden Representations
Yubo Zhu, Dongrui Liu, Zecheng Lin +3
Estimating the difficulty of input questions as perceived by large language models (LLMs) is essential for accurate performance evaluation and adaptive inference. Existing methods…
On Evaluating the Poisoning Robustness of Federated Learning under Local Differential Privacy
Zijian Wang, Wei Tong, Tingxuan Han +4
Federated learning (FL) combined with local differential privacy (LDP) enables privacy-preserving model training across decentralized data sources. However, the decentralized data-…
Detecting Dataset Abuse in Fine-Tuning Stable Diffusion Models for Text-to-Image Synthesis
Songrui Wang, Yubo Zhu, Wei Tong +1
Text-to-image synthesis has become highly popular for generating realistic and stylized images, often requiring fine-tuning generative models with domain-specific datasets for spec…
Data Poisoning Attacks to Locally Differentially Private Frequent Itemset Mining Protocols
Wei Tong, Haoyu Chen, Jiacheng Niu +1
Local differential privacy (LDP) provides a way for an untrusted data collector to aggregate users' data without violating their privacy. Various privacy-preserving data analysis t…
The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in Prompts
Zhili Shen, Zihang Xi, Ying He +3
The rapid adoption of online chatbots represents a significant advancement in artificial intelligence. However, this convenience brings considerable privacy concerns, as prompts ca…
SGBA: A Stealthy Scapegoat Backdoor Attack against Deep Neural Networks
Ying He, Zhili Shen, Chang Xia +3
Outsourced deep neural networks have been demonstrated to suffer from patch-based trojan attacks, in which an adversary poisons the training sets to inject a backdoor in the obtain…