4 citations · 16 across the 43 of their papers we have counts for
22 papers · 1 filter
Aphanta: Diagnosing Task-Aligned Image-Edited Intermediates for Multimodal Reasoning
Hengyuan Xu, Wei Cheng, Yumeng Ji +4
Explicit visual intermediates can help multimodal large language models (MLLMs) externalize spatial evidence and updated visual states, but their utility depends on whether an imag…
WithEveryone: Unified Planning and Identity Grounding for Group Image Generation
Hengyuan Xu, Qixun Wang, Yiji Cheng +5
Identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people. Beyond retaining each identity, the model must bind every refe…
Adversarial Orthogonal Disentanglement for LVLM Hallucination Mitigation
Ruoxi Cheng, Haoxuan Ma, Zhengfei Hai +6
Large Vision-Language Models (LVLMs) have advanced multimodal understanding, yet their reliability is limited by hallucination, where generated content conflicts with visual facts.…
TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models
Xin Wang, Yixu Wang, Jiaming Zhang +6
Large-scale pre-trained Vision-Language models (VLMs), such as CLIP, exhibit strong zero-shot generalization, yet remain highly vulnerable to imperceptible adversarial perturbation…
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…
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…
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…
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-…
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…
BadPatch: Diffusion-Based Generation of Physical Adversarial Patches
Zhixiang Wang, Xingjun Ma, Yu-Gang Jiang
Physical adversarial patches printed on clothing can enable individuals to evade person detectors, but most existing methods prioritize attack effectiveness over stealthiness, resu…
IDEATOR: Jailbreaking and Benchmarking Large Vision-Language Models Using Themselves
Ruofan Wang, Juncheng Li, Yixu Wang +6
As large Vision-Language Models (VLMs) gain prominence, ensuring their safe deployment has become critical. Recent studies have explored VLM robustness against jailbreak attacks-te…
UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation
Ye Sun, Hao Zhang, Tiehua Zhang +2
Image segmentation is a crucial vision task that groups pixels within an image into semantically meaningful segments, which is pivotal in obtaining a fine-grained understanding of…
BlueSuffix: Reinforced Blue Teaming for Vision-Language Models Against Jailbreak Attacks
Yunhan Zhao, Xiang Zheng, Lin Luo +3
In this paper, we focus on black-box defense for VLMs against jailbreak attacks. Existing black-box defense methods are either unimodal or bimodal. Unimodal methods enhance either…
AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning
Xin Wang, Kai Chen, Xingjun Ma +3
Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks even under a black-box setting where the adversary can only query the model. Particularly, query-based…
Downstream Transfer Attack: Adversarial Attacks on Downstream Models with Pre-trained Vision Transformers
Weijie Zheng, Xingjun Ma, Hanxun Huang +2
With the advancement of vision transformers (ViTs) and self-supervised learning (SSL) techniques, pre-trained large ViTs have become the new foundation models for computer vision a…
A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models
Xincheng Shuai, Henghui Ding, Xingjun Ma +3
Image editing aims to edit the given synthetic or real image to meet the specific requirements from users. It is widely studied in recent years as a promising and challenging field…
White-box Multimodal Jailbreaks Against Large Vision-Language Models
Ruofan Wang, Xingjun Ma, Hanxu Zhou +3
Recent advancements in Large Vision-Language Models (VLMs) have underscored their superiority in various multimodal tasks. However, the adversarial robustness of VLMs has not been…
Adversarial Prompt Tuning for Vision-Language Models
Jiaming Zhang, Xingjun Ma, Xin Wang +4
With the rapid advancement of multimodal learning, pre-trained Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable capacities in bridging the gap between visual…