5 citations · 16 across the 13 of their papers we have counts for
13 papers
InfraPatch: Cross-Task Targeted Grayscale Patch Attacks on Infrared-Adapted Vision-Language Models
Chengyin Hu, Dingyi Lu, Jiaju Han +5
Infrared vision-language models (IR-VLMs) have emerged as a promising paradigm for multimodal perception under low-visibility conditions, yet their robustness to targeted adversari…
A Semantic Decoupling-Based Two-Stage Rainy-Day Attack for Revealing Weather Robustness Deficiencies in Vision-Language Models
Chengyin Hu, Xiang Chen, Zhe Jia +4
Vision-Language Models (VLMs) are trained on image-text pairs collected under canonical visual conditions and achieve strong performance on multimodal tasks. However, their robustn…
Multi-View Black-Box Physical Attacks on Infrared Pedestrian Detectors Using Adversarial Infrared Grid
Kalibinuer Tiliwalidi, Chengyin Hu, Weiwen Shi
While extensive research exists on physical adversarial attacks within the visible spectrum, studies on such techniques in the infrared spectrum are limited. Infrared object detect…
Adversarial Infrared Curves: An Attack on Infrared Pedestrian Detectors in the Physical World
Chengyin Hu, Weiwen Shi
Deep neural network security is a persistent concern, with considerable research on visible light physical attacks but limited exploration in the infrared domain. Existing approach…
Two-stage optimized unified adversarial patch for attacking visible-infrared cross-modal detectors in the physical world
Chengyin Hu, Weiwen Shi
Currently, many studies have addressed security concerns related to visible and infrared detectors independently. In practical scenarios, utilizing cross-modal detectors for tasks…
Impact of Light and Shadow on Robustness of Deep Neural Networks
Chengyin Hu, Weiwen Shi, Chao Li +4
Deep neural networks (DNNs) have made remarkable strides in various computer vision tasks, including image classification, segmentation, and object detection. However, recent resea…
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…
Adversarial Catoptric Light: An Effective, Stealthy and Robust Physical-World Attack to DNNs
Chengyin Hu, Weiwen Shi
Deep neural networks (DNNs) have demonstrated exceptional success across various tasks, underscoring the need to evaluate the robustness of advanced DNNs. However, traditional meth…
Adversarial Color Projection: A Projector-based Physical Attack to DNNs
Chengyin Hu, Weiwen Shi, Ling Tian
Recent research has demonstrated that deep neural networks (DNNs) are vulnerable to adversarial perturbations. Therefore, it is imperative to evaluate the resilience of advanced DN…
Impact of Colour Variation on Robustness of Deep Neural Networks
Chengyin Hu, Weiwen Shi
Deep neural networks (DNNs) have have shown state-of-the-art performance for computer vision applications like image classification, segmentation and object detection. Whereas rece…
Adversarial Color Film: Effective Physical-World Attack to DNNs
Chengyin Hu, Weiwen Shi
It is well known that the performance of deep neural networks (DNNs) is susceptible to subtle interference. So far, camera-based physical adversarial attacks haven't gotten much at…
Impact of Scaled Image on Robustness of Deep Neural Networks
Chengyin Hu, Weiwen Shi
Deep neural networks (DNNs) have been widely used in computer vision tasks like image classification, object detection and segmentation. Whereas recent studies have shown their vul…
Adversarial Neon Beam: A Light-based Physical Attack to DNNs
Chengyin Hu, Weiwen Shi, Wen Li
In the physical world, deep neural networks (DNNs) are impacted by light and shadow, which can have a significant effect on their performance. While stickers have traditionally bee…