CAMOU: Learning physical vehicle camouflages to adversarially attack detectors in the wild
In this paper, we conduct an intriguing experimental study about the physical adversarial
attack on object detectors in the wild. In particular, we learn a camouflage pattern to hide …
attack on object detectors in the wild. In particular, we learn a camouflage pattern to hide …
Towards a robust adversarial patch attack against unmanned aerial vehicles object detection
Object detection techniques for autonomous Un-manned Aerial Vehicles (UAV) are built
upon Deep Neural Networks (DNN), which are known to be vulnerable to adversarial patch …
upon Deep Neural Networks (DNN), which are known to be vulnerable to adversarial patch …
[PDF][PDF] Camou: Learning a vehicle camouflage for physical adversarial attack on object detections in the wild
Y Zhang, PDH Foroosh, B Gong - ICLR, 2019 - scholar.archive.org
In this paper, we conduct an intriguing experimental study about the physical adversarial
attack on object detectors in the wild. In particular, we learn a camouflage pattern to hide …
attack on object detectors in the wild. In particular, we learn a camouflage pattern to hide …
On the robustness of semantic segmentation models to adversarial attacks
Abstract Deep Neural Networks (DNNs) have been demonstrated to perform exceptionally
well on most recognition tasks such as image classification and segmentation. However …
well on most recognition tasks such as image classification and segmentation. However …
Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving
M Pourkeshavarz, M Sabokrou… - Proceedings of the …, 2024 - openaccess.thecvf.com
In autonomous driving behavior prediction is fundamental for safe motion planning hence
the security and robustness of prediction models against adversarial attacks are of …
the security and robustness of prediction models against adversarial attacks are of …
Adversarial attacks for image segmentation on multiple lightweight models
Due to the powerful ability of data fitting, deep neural networks have been applied in a wide
range of applications in many key areas. However, in recent years, it was found that some …
range of applications in many key areas. However, in recent years, it was found that some …
The attack generator: A systematic approach towards constructing adversarial attacks
Most state-of-the-art machine learning (ML) classification systems are vulnerable to
adversarial perturbations. As a consequence, adversarial robustness poses a significant …
adversarial perturbations. As a consequence, adversarial robustness poses a significant …
Characterizing adversarial examples based on spatial consistency information for semantic segmentation
Abstract Deep Neural Networks (DNNs) have been widely applied in various recognition
tasks. However, recently DNNs have been shown to be vulnerable against adversarial …
tasks. However, recently DNNs have been shown to be vulnerable against adversarial …
Adversarial attack and defense of yolo detectors in autonomous driving scenarios
J Im Choi, Q Tian - 2022 IEEE Intelligent Vehicles Symposium …, 2022 - ieeexplore.ieee.org
Visual detection is a key task in autonomous driving, and it serves as a crucial foundation for
self-driving planning and control. Deep neural networks have achieved promising results in …
self-driving planning and control. Deep neural networks have achieved promising results in …
Robust physical-world attacks on deep learning visual classification
Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to
adversarial examples, resulting from small-magnitude perturbations added to the input …
adversarial examples, resulting from small-magnitude perturbations added to the input …