5 papers · 1 filter
Covert Visual Prompt Injection against Commercial Multimodal Large Language Models
Meiwen Ding, Song Xia, Chenqi Kong +1
Although multimodal large language models (MLLMs) are increasingly deployed in real-world applications, their instruction-following behavior leaves them vulnerable to prompt inject…
SAKED: Mitigating Hallucination in Large Vision-Language Models via Stability-Aware Knowledge Enhanced Decoding
Zhaoxu Li, Chenqi Kong, Peijun Bao +5
Hallucinations in Large Vision-Language Models (LVLMs) pose significant security and reliability risks in real-world applications. Inspired by the observation that humans are more…
From Pretrain to Pain: Adversarial Vulnerability of Video Foundation Models Without Task Knowledge
Hui Lu, Yi Yu, Song Xia +5
Large-scale Video Foundation Models (VFMs) has significantly advanced various video-related tasks, either through task-specific models or Multi-modal Large Language Models (MLLMs).…
Open-set Anomaly Segmentation in Complex Scenarios
Song Xia, Yi Yu, Henghui Ding +4
Precise segmentation of out-of-distribution (OoD) objects, herein referred to as anomalies, is crucial for the reliable deployment of semantic segmentation models in open-set, safe…
Mitigating the Curse of Dimensionality for Certified Robustness via Dual Randomized Smoothing
Song Xia, Yi Yu, Xudong Jiang +1
Randomized Smoothing (RS) has been proven a promising method for endowing an arbitrary image classifier with certified robustness. However, the substantial uncertainty inherent in…