Results 11 to 20 of about 8,712 (199)
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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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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Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches
CoRR, 2021 9 pages, 7 ...
Nan Ji +4 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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PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 Accepted to WACV ...
Ke Xu +4 more
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GUAP: Graph Universal Attack Through Adversarial Patching
CoRR, 2023 Graph neural networks (GNNs) are a class of effective deep learning models for node classification tasks; yet their predictive capability may be severely compromised under adversarially designed unnoticeable perturbations to the graph structure and/or node data.
Xiao Zang, Jie Chen 0007, Bo Yuan 0001
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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
doaj +1 more source
Detecting Patch Adversarial Attacks with Image Residuals
CoRR, 2020 We introduce an adversarial sample detection algorithm based on image residuals, specifically designed to guard against patch-based attacks. The image residual is obtained as the difference between an input image and a denoised version of it, and a discriminator is trained to distinguish between clean and adversarial samples.
Marius Arvinte +2 more
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Double adversarial attack against license plate recognition system
网络与信息安全学报, 2023 Recent studies have revealed that deep neural networks (DNN) used in artificial intelligence systems are highly vulnerable to adversarial sample-based attacks.To address this issue, a dual adversarial attack method was proposed for license plate ...
Xianyi CHEN, Jun GU1, Kai YAN, Dong JIANG, Linfeng XU, Zhangjie FU
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Brightness-Restricted Adversarial Attack Patch
CoRR, 2023 Adversarial attack patches have gained increasing attention due to their practical applicability in physical-world scenarios. However, the bright colors used in attack patches represent a significant drawback, as they can be easily identified by human observers.
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