1 citations · 1 across the 6 of their papers we have counts for
7 papers
No Way To Steal My Face: Proactive Defense Against Identity-Preserving Personalized Generation
Lizhi Xiong, Jun Li, Ziqiang Li +2
Recent advances in diffusion models have enabled high-fidelity, identity-preserving image generation for personalized applications such as digital avatars and virtual try-on system…
Robust Coverless Linguistic Steganography via Sentence Embedding Space with Global Resynchronization
Lizhi Xiong, Yuping Lu, Jun Li +2
Linguistic steganography enables covert communication through natural language. Existing methods heavily rely on token-level operations and struggle to maintain reliability under w…
Beyond Text Prompts: Precise Concept Erasure through Text-Image Collaboration
Jun Li, Lizhi Xiong, Ziqiang Li +4
Text-to-image generative models have achieved impressive fidelity and diversity, but can inadvertently produce unsafe or undesirable content due to implicit biases embedded in larg…
Scaling Exposes the Trigger: Input-Level Backdoor Detection in Text-to-Image Diffusion Models via Cross-Attention Scaling
Zida Li, Jun Li, Yuzhe Sha +3
Text-to-image (T2I) diffusion models have achieved remarkable success in image synthesis, but their reliance on large-scale data and open ecosystems introduces serious backdoor sec…
Enhancing Supervised Composed Image Retrieval via Reasoning-Augmented Representation Engineering
Jun Li, Hongjian Dou, Zhenyu Zhang +3
Composed Image Retrieval (CIR) presents a significant challenge as it requires jointly understanding a reference image and a modified textual instruction to find relevant target im…
A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models
Ziqiang Li, Jun Li, Lizhi Xiong +2
Text-to-image diffusion models have made significant advancements in generating high-quality, diverse images from text prompts. However, the inherent limitations of textual signals…
Organizing Unstructured Image Collections using Natural Language
Mingxuan Liu, Zhun Zhong, Jun Li +3
In this work, we introduce and study the novel task of Open-ended Semantic Multiple Clustering (OpenSMC). Given a large, unstructured image collection, the goal is to automatically…