Results 31 to 40 of about 31,109 (263)
Remote Sensing, 2023 Deep neural networks (DNNs) can improve the image analysis and interpretation of remote sensing technology by extracting valuable information from images, and has extensive applications such as military affairs, agriculture, environment, transportation ...
Binyue Deng +5 more
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Adversarial example defense algorithm for MNIST based on image reconstruction
网络与信息安全学报, 2022 With the popularization of deep learning, more and more attention has been paid to its security issues.The adversarial sample is to add a small disturbance to the original image, which can cause the deep learning model to misclassify the image, which ...
Zhongyuan QIN +3 more
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On Physical Adversarial Patches for Object Detection
CoRR, 2019 In this paper, we demonstrate a physical adversarial patch attack against object detectors, notably the YOLOv3 detector. Unlike previous work on physical object detection attacks, which required the patch to overlap with the objects being misclassified or avoiding detection, we show that a properly designed patch can suppress virtually all the detected
Mark Lee, J. Zico Kolter
openaire +2 more sources
IEEE AccessRecent studies have shown that machine-learning models are vulnerable to adversarial attacks. Adversarial attacks are deliberate attempts to modify the input data of a machine learning model in a way that causes it to produce incorrect predictions.
Palakorn Kamnounsing +3 more
doaj +1 more source
Extended Spatially Localized Perturbation GAN (eSLP-GAN) for Robust Adversarial Camouflage Patches
Sensors, 2021 Deep neural networks (DNNs), especially those used in computer vision, are highly vulnerable to adversarial attacks, such as adversarial perturbations and adversarial patches. Adversarial patches, often considered more appropriate for a real-world attack,
Yongsu Kim +5 more
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The Missing Data Encoder: Cross-Channel Image Completion\\with Hide-And-Seek Adversarial Network
, 2019 Image completion is the problem of generating whole images from fragments only. It encompasses inpainting (generating a patch given its surrounding), reverse inpainting/extrapolation (generating the periphery given the central patch) as well as ...
Cord, Matthieu +2 more
core +1 more source
Transferable adversarial masked self-distillation for unsupervised domain adaptation
Complex & Intelligent Systems, 2023 Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to a related unlabeled target domain. Most existing works focus on minimizing the domain discrepancy to learn global domain-invariant representation using CNN ...
Yuelong Xia, Li-Jun Yun, Chengfu Yang
doaj +1 more source
Adversarial Inpainting of Medical Image Modalities
, 2018 Numerous factors could lead to partial deteriorations of medical images. For example, metallic implants will lead to localized perturbations in MRI scans.
Armanious, Karim +3 more
core +1 more source
Traversing the subspace of adversarial patches
Machine Vision and ApplicationsAbstract Despite ongoing research on the topic of adversarial examples in deep learning for computer vision, some fundamentals of the nature of these attacks remain unclear. As the manifold hypothesis posits, high-dimensional data tends to be part of a low-dimensional manifold.
Jens Bayer +4 more
openaire +3 more sources
Distributional Modeling for Location-Aware Adversarial Patches
CoRR, 2023 Adversarial patch is one of the important forms of performing adversarial attacks in the physical world. To improve the naturalness and aggressiveness of existing adversarial patches, location-aware patches are proposed, where the patch's location on the target object is integrated into the optimization process to perform attacks.
Xingxing Wei 0001 +3 more
openaire +2 more sources