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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 Camera Patch: An Effective and Robust Physical-World Attack on Object Detectors
Kalibinuer Tiliwalidi
Nowadays, the susceptibility of deep neural networks (DNNs) has garnered significant attention. Researchers are exploring patch-based physical attacks, yet traditional approaches,…
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…