2 citations · 7 across the 35 of their papers we have counts for
22 papers · 1 filter
Universal Cross-Prompt Adversarial Attacks on Promptable Concept Segmentation
Ziqi Zhou, Yifan Hu, Yufei Song +5
The Segment Anything Model (SAM) achieves remarkable performance in visual segmentation. The latest SAM3 extends promptable segmentation to concept-level prediction, broadening the…
Imperceptible and Reversible Adversarial Examples against Vision-Language Models for Privacy Protection
Qi Lu, Ziqi Zhou, Yufei Song +5
Vision Language Models (VLMs) offer powerful multimodal ability but also expose users to text-based privacy attacks where adversaries crawl online photos and query VLMs to extract…
Dual-branch Robust Unlearnable Examples
Xianlong Wang, Hangtao Zhang, Wenbo Pan +4
Unlearnable examples (UEs) aim to compromise model training by injecting imperceptible perturbations to clean samples. However, existing UE schemes exhibit limited robustness again…
VFACamou: View-Fused Adversarial Camouflage for Environment-Adaptive Physical Evasion
Shihui Yan, Hu Liu, Junyu Shi +6
Adversarial camouflage in the physical world remains highly challenging, particularly under UAV reconnaissance where targets undergo continuous geometric changes and extreme illumi…
Transferable Physical-World Adversarial Patches Against Object Detection in Autonomous Driving
Zihui Zhu, Ziqi Zhou, Yichen Wang +3
Deep learning drives major advances in autonomous driving (AD), where object detectors are central to perception. However, adversarial attacks pose significant threats to the relia…
Transferable Physical-World Adversarial Patches Against Pedestrian Detection Models
Shihui Yan, Ziqi Zhou, Yufei Song +3
Physical adversarial patch attacks critically threaten pedestrian detection, causing surveillance and autonomous driving systems to miss pedestrians and creating severe safety risk…
Erosion Attack for Adversarial Training to Enhance Semantic Segmentation Robustness
Yufei Song, Ziqi Zhou, Menghao Deng +4
Existing segmentation models exhibit significant vulnerability to adversarial attacks.To improve robustness, adversarial training incorporates adversarial examples into model train…
UFVideo: Towards Unified Fine-Grained Video Cooperative Understanding with Large Language Models
Hewen Pan, Cong Wei, Dashuang Liang +8
With the advancement of multi-modal Large Language Models (LLMs), Video LLMs have been further developed to perform on holistic and specialized video understanding. However, existi…
Vanish into Thin Air: Cross-prompt Universal Adversarial Attacks for SAM2
Ziqi Zhou, Yifan Hu, Yufei Song +6
Recent studies reveal the vulnerability of the image segmentation foundation model SAM to adversarial examples. Its successor, SAM2, has attracted significant attention due to its…
SegTrans: Transferable Adversarial Examples for Segmentation Models
Yufei Song, Ziqi Zhou, Qi Lu +6
Segmentation models exhibit significant vulnerability to adversarial examples in white-box settings, but existing adversarial attack methods often show poor transferability across…
DarkHash: A Data-Free Backdoor Attack Against Deep Hashing
Ziqi Zhou, Menghao Deng, Yufei Song +6
Benefiting from its superior feature learning capabilities and efficiency, deep hashing has achieved remarkable success in large-scale image retrieval. Recent studies have demonstr…
Towards Real-World Deepfake Detection: A Diverse In-the-wild Dataset of Forgery Faces
Junyu Shi, Minghui Li, Junguo Zuo +8
Deepfakes, leveraging advanced AIGC (Artificial Intelligence-Generated Content) techniques, create hyper-realistic synthetic images and videos of human faces, posing a significant…
ADVEDM:Fine-grained Adversarial Attack against VLM-based Embodied Agents
Yichen Wang, Hangtao Zhang, Hewen Pan +7
Vision-Language Models (VLMs), with their strong reasoning and planning capabilities, are widely used in embodied decision-making (EDM) tasks in embodied agents, such as autonomous…
Test-Time Backdoor Detection for Object Detection Models
Hangtao Zhang, Yichen Wang, Shihui Yan +7
Object detection models are vulnerable to backdoor attacks, where attackers poison a small subset of training samples by embedding a predefined trigger to manipulate prediction. De…
PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation
Yufei Song, Ziqi Zhou, Minghui Li +6
With the rapid advancement of deep learning, the model robustness has become a significant research hotspot, \ie, adversarial attacks on deep neural networks. Existing works primar…
Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature
Yichen Wang, Yuxuan Chou, Ziqi Zhou +4
As deep neural networks (DNNs) are widely applied in the physical world, many researches are focusing on physical-world adversarial examples (PAEs), which introduce perturbations t…
NumbOD: A Spatial-Frequency Fusion Attack Against Object Detectors
Ziqi Zhou, Bowen Li, Yufei Song +6
With the advancement of deep learning, object detectors (ODs) with various architectures have achieved significant success in complex scenarios like autonomous driving. Previous ad…
Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need
Xianlong Wang, Minghui Li, Wei Liu +5
Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity informa…
Transferable Adversarial Facial Images for Privacy Protection
Minghui Li, Jiangxiong Wang, Hao Zhang +3
The success of deep face recognition (FR) systems has raised serious privacy concerns due to their ability to enable unauthorized tracking of users in the digital world. Previous s…
Detector Collapse: Physical-World Backdooring Object Detection to Catastrophic Overload or Blindness in Autonomous Driving
Hangtao Zhang, Shengshan Hu, Yichen Wang +5
Object detection tasks, crucial in safety-critical systems like autonomous driving, focus on pinpointing object locations. These detectors are known to be susceptible to backdoor a…
Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples
Ziqi Zhou, Minghui Li, Wei Liu +7
With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trai…
Detecting and Corrupting Convolution-based Unlearnable Examples
Minghui Li, Xianlong Wang, Zhifei Yu +4
Convolution-based unlearnable examples (UEs) employ class-wise multiplicative convolutional noise to training samples, severely compromising model performance. This fire-new type o…