2 citations · 2 across the 22 of their papers we have counts for
7 papers · 2 filters
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…