4 citations · 16 across the 43 of their papers we have counts for
15 papers · 1 filter
WithAnyone: Towards Controllable and ID Consistent Image Generation
Hengyuan Xu, Wei Cheng, Peng Xing +8
Identity-consistent generation has become an important focus in text-to-image research, with recent models achieving notable success in producing images aligned with a reference id…
FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models
Kun Zhai, Siheng Chen, Xingjun Ma +1
Federated Prompt Tuning (FPT) is an efficient method for cross-client collaborative fine-tuning of large Vision-Language Models (VLMs). However, models tuned using FPT are vulnerab…
T2UE: Generating Unlearnable Examples from Text Descriptions
Xingjun Ma, Hanxun Huang, Tianwei Song +3
Large-scale pre-training frameworks like CLIP have revolutionized multimodal learning, but their reliance on web-scraped datasets, frequently containing private user data, raises s…
RedTopic: Toward Topic-Diverse Red Teaming of Large Language Models
Jiale Ding, Xiang Zheng, Yutao Wu +5
As large language models (LLMs) are increasingly deployed as black-box components in real-world applications, red teaming has become essential for identifying potential risks. It t…
BrokenVideos: A Benchmark Dataset for Fine-Grained Artifact Localization in AI-Generated Videos
Jiahao Lin, Weixuan Peng, Bojia Zi +4
Recent advances in deep generative models have led to significant progress in video generation, yet the fidelity of AI-generated videos remains limited. Synthesized content often e…
DAVID-XR1: Detecting AI-Generated Videos with Explainable Reasoning
Yifeng Gao, Yifan Ding, Hongyu Su +9
As AI-generated video becomes increasingly pervasive across media platforms, the ability to reliably distinguish synthetic content from authentic footage has become both urgent and…
GenBreak: Red Teaming Text-to-Image Generators Using Large Language Models
Zilong Wang, Xiang Zheng, Xiaosen Wang +3
Text-to-image (T2I) models such as Stable Diffusion have advanced rapidly and are now widely used in content creation. However, these models can be misused to generate harmful cont…
SafeVid: Toward Safety Aligned Video Large Multimodal Models
Yixu Wang, Jiaxin Song, Yifeng Gao +6
As Video Large Multimodal Models (VLMMs) rapidly advance, their inherent complexity introduces significant safety challenges, particularly the issue of mismatched generalization wh…
JailBound: Jailbreaking Internal Safety Boundaries of Vision-Language Models
Jiaxin Song, Yixu Wang, Jie Li +4
Vision-Language Models (VLMs) exhibit impressive performance, yet the integration of powerful vision encoders has significantly broadened their attack surface, rendering them incre…
SAMA: Towards Multi-Turn Referential Grounded Video Chat with Large Language Models
Ye Sun, Hao Zhang, Henghui Ding +3
Achieving fine-grained spatio-temporal understanding in videos remains a major challenge for current Video Large Multimodal Models (Video LMMs). Addressing this challenge requires…
Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks
Yixin Cao, Shibo Hong, Xinze Li +24
Large Language Models (LLMs) are advancing at an amazing speed and have become indispensable across academia, industry, and daily applications. To keep pace with the status quo, th…
OmniSVG: A Unified Scalable Vector Graphics Generation Model
Yiying Yang, Wei Cheng, Sijin Chen +7
Scalable Vector Graphics (SVG) is an important image format widely adopted in graphic design because of their resolution independence and editability. The study of generating high-…
Identity Lock: Locking API Fine-tuned LLMs With Identity-based Wake Words
Hongyu Su, Yifeng Gao, Yifan Ding +1
The rapid advancement of Large Language Models (LLMs) has increased the complexity and cost of fine-tuning, leading to the adoption of API-based fine-tuning as a simpler and more e…
Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety
Xingjun Ma, Yifeng Gao, Yixu Wang +45
The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has reshaped the landscape of Artifici…
AIM: Additional Image Guided Generation of Transferable Adversarial Attacks
Teng Li, Xingjun Ma, Yu-Gang Jiang
Transferable adversarial examples highlight the vulnerability of deep neural networks (DNNs) to imperceptible perturbations across various real-world applications. While there have…