2 citations · 4 across the 12 of their papers we have counts for
12 papers
Weak Ties, Strong Signals: Efficient Training Data Detection in Diffusion LLMs via Independent Token Sampling
Hongyao Yu, Tianqu Zhuang, Ziyuan Xu +4
Diffusion large language models (dLLMs) offer a compelling alternative to autoregressive models, yet they may expose sensitive training data during denoising. Detecting such usage…
Beyond Illumination: A Conditional Mutual Information-Guided Network for Low-Light Image Enhancement
Ya-nan Guan, Shaonan Zhang, Tao Dai +5
Low-light image enhancement (LLIE) seeks to restore structural fidelity, natural color rendition, and proper exposure from images captured under inadequate lighting conditions. Rec…
NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: AI Flash Portrait (Track 3)
Ya-nan Guan, Shaonan Zhang, Hang Guo +55
In this paper, we present a comprehensive overview of the NTIRE 2026 3rd Restore Any Image Model (RAIM) challenge, with a specific focus on Track 3: AI Flash Portrait. Despite sign…
Seeing Through the Chain: Understanding and Mitigating Hallucinations in Multimodal Large Reasoning Models
Hao Fang, Jinyu Li, Jiawei Kong +4
While multimodal reasoning models (MLRMs) have exhibited impressive capabilities, they remain prone to hallucinations, and effective solutions are still underexplored. In this pape…
Towards Distillation-Resistant Large Language Models: An Information-Theoretic Perspective
Hao Fang, Tianyi Zhang, Tianqu Zhuang +6
Proprietary large language models (LLMs) embody substantial economic value and are generally exposed only as black-box APIs, yet adversaries can still exploit their outputs to extr…
LFM4Ads: Large Foundation Model for Ads Recommendation
Shangyu Zhang, Shijie Quan, Zhongren Wang +32
Recent work leverages large-scale pretrained foundation models (FMs) to transfer generalizable representations to downstream advertising tasks. Most existing approaches transfer us…
ICAS: Detecting Training Data from Autoregressive Image Generative Models
Hongyao Yu, Yixiang Qiu, Yiheng Yang +6
Autoregressive image generation has witnessed rapid advancements, with prominent models such as scale-wise visual auto-regression pushing the boundaries of visual synthesis. Howeve…
Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors
Hao Fang, Jiawei Kong, Tianqu Zhuang +6
The misuse of large language models (LLMs), such as academic plagiarism, has driven the development of detectors to identify LLM-generated texts. To bypass these detectors, paraphr…
Editable-DeepSC: Reliable Cross-Modal Semantic Communications for Facial Editing
Bin Chen, Wenbo Yu, Qinshan Zhang +4
Interactive computer vision (CV) plays a crucial role in various real-world applications, whose performance is highly dependent on communication networks. Nonetheless, the data-ori…
Rank Matters: Understanding and Defending Model Inversion Attacks via Low-Rank Feature Filtering
Hongyao Yu, Yixiang Qiu, Hao Fang +6
Model Inversion Attacks (MIAs) pose a significant threat to data privacy by reconstructing sensitive training samples from the knowledge embedded in trained machine learning models…
MIBench: A Comprehensive Framework for Benchmarking Model Inversion Attack and Defense
Yixiang Qiu, Hongyao Yu, Hao Fang +6
Model Inversion (MI) attacks aim at leveraging the output information of target models to reconstruct privacy-sensitive training data, raising critical concerns regarding the priva…
A Noise-tolerant Differentiable Learning Approach for Single Occurrence Regular Expression with Interleaving
Rongzhen Ye, Tianqu Zhuang, Hai Wan +3
We study the problem of learning a single occurrence regular expression with interleaving (SOIRE) from a set of text strings possibly with noise. SOIRE fully supports interleaving…