5 citations · 16 across the 13 of their papers we have counts for
6 papers · 1 filter
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…