5 citations · 24 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
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…