Results 21 to 30 of about 619 (184)
Bilateral Adversarial Patch Generating Network for the Object Tracking Algorithm
Remote Sensing, 2023 Deep learning-based algorithms for single object tracking (SOT) have shown impressive performance but remain susceptible to adversarial patch attacks. However, existing adversarial patch generation methods primarily focus on generating patches within the
Jarhinbek Rasol +4 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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Surreptitious Adversarial Examples through Functioning QR Code
Journal of Imaging, 2022 The continuous advances in the technology of Convolutional Neural Network (CNN) and Deep Learning have been applied to facilitate various tasks of human life.
Aran Chindaudom +3 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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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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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
doaj +3 more sources
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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Patch-wise++ Perturbation for Adversarial Targeted Attacks
CoRR, 2020 Although great progress has been made on adversarial attacks for deep neural networks (DNNs), their transferability is still unsatisfactory, especially for targeted attacks. There are two problems behind that have been long overlooked: 1) the conventional setting of $T$ iterations with the step size of $ε/T$ to comply with the $ε$-constraint.
Lianli Gao +3 more
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