4 citations · 14 across the 29 of their papers we have counts for
19 papers · 1 filter
AdvTiles: Physical Adversarial Camouflage Clothing against Person Detectors via Learnable Tiles
Jinlei Wang, Jiahuan Long, Mingkai Sun +9
Physical adversarial attacks against person detectors have evolved from localized patches to full-body textures. However, achieving both visual naturalness and strong attack effect…
Challenging Vision-Language Models with Physically Deployable Multimodal Semantic Lighting Attacks
Yingying Zhao, Chengyin Hu, Qike Zhang +7
Vision-Language Models (VLMs) have shown remarkable performance, yet their security remains insufficiently understood. Existing adversarial studies focus almost exclusively on the…
Revealing Physical-World Semantic Vulnerabilities: Universal Adversarial Patch for Infrared Vision-Language Models
Chengyin Hu, Yuxian Dong, Yikun Guo +8
Infrared vision-language models (IR-VLMs) are becoming important for semantic perception in low-visibility environments, yet their robustness to physical semantic attacks remains u…
PapMOT: Exploring Adversarial Patch Attack against Multiple Object Tracking
Jiahuan Long, Tingsong Jiang, Wen Yao +5
Tracking multiple objects in a continuous video stream is crucial for many computer vision tasks. It involves detecting and associating objects with their respective identities acr…
Robust SAM: On the Adversarial Robustness of Vision Foundation Models
Jiahuan Long, Zhengqin Xu, Tingsong Jiang +4
The Segment Anything Model (SAM) is a widely used vision foundation model with diverse applications, including image segmentation, detection, and tracking. Given SAM's wide applica…
CDUPatch: Color-Driven Universal Adversarial Patch Attack for Dual-Modal Visible-Infrared Detectors
Jiahuan Long, Wen Yao, Tingsong Jiang +1
Adversarial patches are widely used to evaluate the robustness of object detection systems in real-world scenarios. These patches were initially designed to deceive single-modal de…
Parameter-Free Fine-tuning via Redundancy Elimination for Vision Foundation Models
Jiahuan Long, Tingsong Jiang, Wen Yao +5
Vision foundation models (VFMs) have demonstrated remarkable capabilities in learning universal visual representations. However, adapting these models to downstream tasks conventio…
Improving the Transferability of Adversarial Examples by Feature Augmentation
Donghua Wang, Wen Yao, Tingsong Jiang +3
Despite the success of input transformation-based attacks on boosting adversarial transferability, the performance is unsatisfying due to the ignorance of the discrepancy across mo…
Universal Multi-view Black-box Attack against Object Detectors via Layout Optimization
Donghua Wang, Wen Yao, Tingsong Jiang +2
Object detectors have demonstrated vulnerability to adversarial examples crafted by small perturbations that can deceive the object detector. Existing adversarial attacks mainly fo…
Adversarial Examples in the Physical World: A Survey
Jiakai Wang, Xianglong Liu, Jin Hu +6
Deep neural networks (DNNs) have demonstrated high vulnerability to adversarial examples, raising broad security concerns about their applications. Besides the attacks in the digit…
Multi-objective Evolutionary Search of Variable-length Composite Semantic Perturbations
Jialiang Sun, Wen Yao, Tingsong Jiang +1
Deep neural networks have proven to be vulnerable to adversarial attacks in the form of adding specific perturbations on images to make wrong outputs. Designing stronger adversaria…
RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical World
Donghua Wang, Wen Yao, Tingsong Jiang +2
Physical adversarial attacks against deep neural networks (DNNs) have recently gained increasing attention. The current mainstream physical attacks use printed adversarial patches…
A Plug-and-Play Defensive Perturbation for Copyright Protection of DNN-based Applications
Donghua Wang, Wen Yao, Tingsong Jiang +3
Wide deployment of deep neural networks (DNNs) based applications (e.g., style transfer, cartoonish), stimulating the requirement of copyright protection of such application's prod…
Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation
Jialiang Sun, Wen Yao, Tingsong Jiang +1
Neural architecture search (NAS) has emerged as one successful technique to find robust deep neural network (DNN) architectures. However, most existing robustness evaluations in NA…
Adversarial Infrared Blocks: A Multi-view Black-box Attack to Thermal Infrared Detectors in Physical World
Chengyin Hu, Weiwen Shi, Tingsong Jiang +3
Infrared imaging systems have a vast array of potential applications in pedestrian detection and autonomous driving, and their safety performance is of great concern. However, few…
Differential Evolution based Dual Adversarial Camouflage: Fooling Human Eyes and Object Detectors
Jialiang Sun, Tingsong Jiang, Wen Yao +2
Recent studies reveal that deep neural network (DNN) based object detectors are vulnerable to adversarial attacks in the form of adding the perturbation to the images, leading to t…
A Survey on Physical Adversarial Attack in Computer Vision
Donghua Wang, Wen Yao, Tingsong Jiang +2
Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-craft feature extraction with its strong feature learning capability, leading to substan…
A Multi-objective Memetic Algorithm for Auto Adversarial Attack Optimization Design
Jialiang Sun, Wen Yao, Tingsong Jiang +1
The phenomenon of adversarial examples has been revealed in variant scenarios. Recent studies show that well-designed adversarial defense strategies can improve the robustness of d…
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack
Donghua Wang, Tingsong Jiang, Jialiang Sun +5
Physical adversarial attacks in object detection have attracted increasing attention. However, most previous works focus on hiding the objects from the detector by generating an in…