SSIM-Based Autoencoder Modeling to Defeat Adversarial Patch Attacks [PDF]
SensorsObject detection systems are used in various fields such as autonomous vehicles and facial recognition. In particular, object detection using deep learning networks enables real-time processing in low-performance edge devices and can maintain high ...
Seungyeol Lee +3 more
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Scale-Adaptive Adversarial Patch Attack for Remote Sensing Image Aircraft Detection
Remote Sensing, 2021 With the adversarial attack of convolutional neural networks (CNNs), we are able to generate adversarial patches to make an aircraft undetectable by object detectors instead of covering the aircraft with large camouflage nets. However, aircraft in remote
Mingming Lu, Qi Li, Li Chen
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Adversarial Patch Attack on Multi-Scale Object Detection for UAV Remote Sensing Images
Remote Sensing, 2022 Although deep learning has received extensive attention and achieved excellent performance in various scenarios, it suffers from adversarial examples to some extent. In particular, physical attack poses a greater threat than digital attack.
Yu Zhang, Jiahao Qi, Kangcheng Bin
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Investigation of the Robustness and Transferability of Adversarial Patches in Multi-View Infrared Target Detection [PDF]
Journal of ImagingThis paper proposes a novel adversarial patch-generation method for infrared images, focusing on enhancing the robustness and transferability of infrared adversarial patches. To improve the flexibility and diversity of the generation process, a Bernoulli
Qing Zhou +7 more
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Localized Query Attack Toward Transformer-Based Visible Object Detectors [PDF]
SensorsTransformer-based detectors have demonstrated exceptional accuracy in visible-object detection tasks. However, adversarial patches, specific types of adversarial examples, can disrupt these detectors by introducing unrestricted perturbations into ...
Yang Wang, Ang Li, Zhen Yang, Xunyun Liu
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Adversarial Patch Attacks on Monocular Depth Estimation Networks [PDF]
IEEE Access, 2020 Thanks to the excellent learning capability of deep convolutional neural networks (CNN), monocular depth estimation using CNNs has achieved great success in recent years.
Koichiro Yamanaka +3 more
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Segment and Recover: Defending Object Detectors Against Adversarial Patch Attacks [PDF]
Journal of ImagingObject detection is used to automatically identify and locate specific objects within images or videos for applications like autonomous driving, security surveillance, and medical imaging.
Haotian Gu, Hamidreza Jafarnejadsani
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FP-ZOO: Fast Patch-Based Zeroth Order Optimization for Black-Box Adversarial Attacks on Vision Models [PDF]
SensorsDeep neural networks have outperformed conventional methods in various fields such as image recognition, natural language processing, and speech recognition.
Junho Seo, Seungho Jeon
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DPatch: An Adversarial Patch Attack on Object Detectors [PDF]
CoRR, 2018 Object detectors have emerged as an indispensable module in modern computer vision systems. In this work, we propose DPatch -- a black-box adversarial-patch-based attack towards mainstream object detectors (i.e. Faster R-CNN and YOLO). Unlike the original adversarial patch that only manipulates image-level classifier, our DPatch simultaneously attacks ...
Xin Liu 0075 +5 more
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Using Frequency Attention to Make Adversarial Patch Powerful Against Person Detector
IEEE Access, 2023 Deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, object detectors may be attacked by applying a particular adversarial patch to the image.
Xiaochun Lei +5 more
doaj +1 more source