Physical adversarial attack meets computer vision: A decade survey
Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision,
their vulnerability to adversarial attacks remains a critical concern. Extensive research has …
their vulnerability to adversarial attacks remains a critical concern. Extensive research has …
A survey on physical adversarial attack in computer vision
Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-
craft feature extraction with its strong feature learning capability, leading to substantial …
craft feature extraction with its strong feature learning capability, leading to substantial …
Multiview robust adversarial stickers for arbitrary objects in the physical world
S Oslund, C Washington, A So… - … of Computational and …, 2022 - ojs.bonviewpress.com
Multiview Robust Adversarial Stickers for Arbitrary Objects in the Physical World Page 1
Received: 13 July 2022 | Revised: 18 July 2022 | Accepted: 24 August 2022 | Published online …
Received: 13 July 2022 | Revised: 18 July 2022 | Accepted: 24 August 2022 | Published online …
Benchmarking adversarial patch against aerial detection
Deep neural networks (DNNs) have become essential for aerial detection. However, DNNs
are vulnerable to adversarial examples, which pose great security concerns for security …
are vulnerable to adversarial examples, which pose great security concerns for security …
Adversarial patch attack on multi-scale object detection for UAV remote sensing images
Y Zhang, Y Zhang, J Qi, K Bin, H Wen, X Tong… - Remote Sensing, 2022 - mdpi.com
Although deep learning has received extensive attention and achieved excellent
performance in various scenarios, it suffers from adversarial examples to some extent. In …
performance in various scenarios, it suffers from adversarial examples to some extent. In …
CBA: Contextual background attack against optical aerial detection in the physical world
J Lian, X Wang, Y Su, M Ma… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Patch-based physical attacks have increasingly aroused concerns. However, most existing
methods focus on obscuring targets captured on the ground, and some of these methods are …
methods focus on obscuring targets captured on the ground, and some of these methods are …
Adversarial patch attacks against aerial imagery object detectors
Abstract Although Deep Neural Networks (DNNs)-based object detectors are widely used in
various fields, especially on aerial imagery object detections, it has been observed that a …
various fields, especially on aerial imagery object detections, it has been observed that a …
A comprehensive study on the robustness of deep learning-based image classification and object detection in remote sensing: Surveying and benchmarking
Deep neural networks (DNNs) have found widespread applications in interpreting remote
sensing (RS) imagery. However, it has been demonstrated in previous works that DNNs are …
sensing (RS) imagery. However, it has been demonstrated in previous works that DNNs are …
Schemes of attacks on machine learning models
D Namiot - International Journal of Open Information Technologies, 2023 - injoit.ru
This article discusses attack schemes on artificial intelligence systems (on machine learning
models). Classically, attacks on machine learning systems are special data modifications at …
models). Classically, attacks on machine learning systems are special data modifications at …
Boosting transferability of physical attack against detectors by redistributing separable attention
Y Zhang, Z Gong, Y Zhang, K Bin, Y Li, J Qi, H Wen… - Pattern Recognition, 2023 - Elsevier
The research on attack transferability is of great importance as it can guide how to conduct
an adversarial attack without knowing any information about target models. However, it …
an adversarial attack without knowing any information about target models. However, it …