2 citations · 2 across the 22 of their papers we have counts for
17 papers · 1 filter
Answer It or Not? Towards Prompt-Adapted Adversarial Images on Vision Language Models
Shunyuan Bai, Yuchen Ren, Le Yang +3
With the advancement in language modeling, research on adversarial images has recently shifted from image classifiers to vision-language models (VLMs). Despite the new, text-prompt…
Detecting Object Hallucinations in Large Vision-Language Models via Cross-Modal Attention Drifts and Mask-Based Verification
Xuanbing Wen, Boxu Chen, Le Yang +4
Despite recent advances in large vision-language models (LVLMs), object hallucination remains a major barrier to their reliable deployment. Existing detection methods often charact…
Detecting Backdoors in Object Detection via Pre-NMS Prediction Distribution Shift
Longtian Wang, Zhengyu Zhao, Chenhao Lin +5
Object detection models deployed in safety-critical applications remain vulnerable to backdoor attacks that cause targeted misbehaviors when a hidden trigger is present. Existing d…
On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline
Yuchen Ren, Zhengyu Zhao, Chenhao Lin +2
Vision-Language Pre-training Models (VLPMs) are known to be vulnerable to adversarial attacks. Recent transferable attacks on VLPMs have followed a common pipeline with complicated…
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…
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…
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…
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…
Nullu: Mitigating Object Hallucinations in Large Vision-Language Models via HalluSpace Projection
Le Yang, Ziwei Zheng, Boxu Chen +3
Recent studies have shown that large vision-language models (LVLMs) often suffer from the issue of object hallucinations (OH). To mitigate this issue, we introduce an efficient met…
Generalizable Targeted Data Poisoning against Varying Physical Objects
Zhizhen Chen, Zhengyu Zhao, Subrat Kishore Dutta +3
Targeted data poisoning (TDP) aims to compromise the model's prediction on a specific (test) target by perturbing a small subset of training data. Existing work on TDP has focused…
SlowPerception: Physical-World Latency Attack against Visual Perception in Autonomous Driving
Chen Ma, Ningfei Wang, Zhengyu Zhao +2
Autonomous Driving (AD) systems critically depend on visual perception for real-time object detection and multiple object tracking (MOT) to ensure safe driving. However, high laten…
A Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authentication
Jingyi Deng, Chenhao Lin, Zhengyu Zhao +4
Deep generative models have demonstrated impressive performance in various computer vision applications, including image synthesis, video generation, and medical analysis. Despite…
ControlLoc: Physical-World Hijacking Attack on Visual Perception in Autonomous Driving
Chen Ma, Ningfei Wang, Zhengyu Zhao +3
Recent research in adversarial machine learning has focused on visual perception in Autonomous Driving (AD) and has shown that printed adversarial patches can attack object detecto…
Exploiting Facial Relationships and Feature Aggregation for Multi-Face Forgery Detection
Chenhao Lin, Fangbin Yi, Hang Wang +3
Face forgery techniques have emerged as a forefront concern, and numerous detection approaches have been proposed to address this challenge. However, existing methods predominantly…