10 citations · 16 across the 30 of their papers we have counts for
18 papers · 1 filter
Pay Less Attention to Function Words for Free Robustness of Vision-Language Models
Qiwei Tian, Chenhao Lin, Zhengyu Zhao +1
To address the trade-off between robustness and performance for robust VLM, we observe that function words could incur vulnerability of VLMs against cross-modal adversarial attacks…
GEO-Detective: Unveiling Location Privacy Risks in Images with LLM Agents
Xinyu Zhang, Yixin Wu, Boyang Zhang +4
Images shared on social media often expose geographic cues. While early geolocation methods required expert effort and lacked generalization, the rise of Large Vision Language Mode…
Privacy on the Fly: A Predictive Adversarial Transformation Network for Mobile Sensor Data
Tianle Song, Chenhao Lin, Yang Cao +5
Mobile motion sensors such as accelerometers and gyroscopes are now ubiquitously accessible by third-party apps via standard APIs. While enabling rich functionalities like activity…
Multi-modal Deepfake Detection and Localization with FPN-Transformer
Chende Zheng, Ruiqi Suo, Zhoulin Ji +4
The rapid advancement of generative adversarial networks (GANs) and diffusion models has enabled the creation of highly realistic deepfake content, posing significant threats to di…
Rethinking Technology Stack Selection with AI Coding Proficiency
Xiaoyu Zhang, Weipeng Jiang, Shiqing Ma +5
Large language models (LLMs) are now an integral part of software development workflows and are reshaping the whole process. However, existing technology selection methods mainly f…
JADES: A Universal Framework for Jailbreak Assessment via Decompositional Scoring
Junjie Chu, Mingjie Li, Ziqing Yang +6
Accurately determining whether a jailbreak attempt has succeeded is a fundamental yet unresolved challenge. Existing evaluation methods rely on misaligned proxy indicators or naive…
Revisiting Adversarial Patch Defenses on Object Detectors: Unified Evaluation, Large-Scale Dataset, and New Insights
Junhao Zheng, Jiahao Sun, Chenhao Lin +6
Developing reliable defenses against patch attacks on object detectors has attracted increasing interest. However, we identify that existing defense evaluations lack a unified and…
D3: Training-Free AI-Generated Video Detection Using Second-Order Features
Chende Zheng, Ruiqi suo, Chenhao Lin +6
The evolution of video generation techniques, such as Sora, has made it increasingly easy to produce high-fidelity AI-generated videos, raising public concern over the disseminatio…
Adversarial Video Promotion Against Text-to-Video Retrieval
Qiwei Tian, Chenhao Lin, Zhengyu Zhao +3
Thanks to the development of cross-modal models, text-to-video retrieval (T2VR) is advancing rapidly, but its robustness remains largely unexamined. Existing attacks against T2VR a…
Concept Unlearning by Modeling Key Steps of Diffusion Process
Chaoshuo Zhang, Chenhao Lin, Zhengyu Zhao +3
Text-to-image diffusion models remain susceptible to generating undesirable or harmful content. Although concept unlearning mitigates this risk, existing methods struggle with a cr…
Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples
Sicong Han, Chenhao Lin, Zhengyu Zhao +6
Adversarial detection protects models from adversarial attacks by refusing suspicious test samples. However, current detection methods often suffer from weak generalization: their…
Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations
Boxu Chen, Ziwei Zheng, Le Yang +4
Large Vision-Language Models (LVLMs) have achieved remarkable success but continue to struggle with object hallucination (OH), generating outputs inconsistent with visual inputs. W…
HCMA: Hierarchical Cross-model Alignment for Grounded Text-to-Image Generation
Hang Wang, Zhi-Qi Cheng, Chenhao Lin +2
Text-to-image synthesis has progressed to the point where models can generate visually compelling images from natural language prompts. Yet, existing methods often fail to reconcil…
Use as Many Surrogates as You Want: Selective Ensemble Attack to Unleash Transferability without Sacrificing Resource Efficiency
Bo Yang, Hengwei Zhang, Jindong Wang +4
In surrogate ensemble attacks, using more surrogate models yields higher transferability but lower resource efficiency. This practical trade-off between transferability and efficie…
ID-Booth: Identity-consistent Face Generation with Diffusion Models
Darian Tomašević, Fadi Boutros, Chenhao Lin +3
Recent advances in generative modeling have enabled the generation of high-quality synthetic data that is applicable in a variety of domains, including face recognition. Here, stat…
Shining Yourself: High-Fidelity Ornaments Virtual Try-on with Diffusion Model
Yingmao Miao, Zhanpeng Huang, Rui Han +3
While virtual try-on for clothes and shoes with diffusion models has gained attraction, virtual try-on for ornaments, such as bracelets, rings, earrings, and necklaces, remains lar…
Improving Adversarial Transferability on Vision Transformers via Forward Propagation Refinement
Yuchen Ren, Zhengyu Zhao, Chenhao Lin +4
Vision Transformers (ViTs) have been widely applied in various computer vision and vision-language tasks. To gain insights into their robustness in practical scenarios, transferabl…
Revisiting Training-Inference Trigger Intensity in Backdoor Attacks
Chenhao Lin, Chenyang Zhao, Shiwei Wang +3
Backdoor attacks typically place a specific trigger on certain training data, such that the model makes prediction errors on inputs with that trigger during inference. Despite the…