Results 31 to 40 of about 5,574 (249)
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 patch camouflage against aerial detection [PDF]
Artificial Intelligence and Machine Learning in Defense Applications II, 2020 Detection of military assets on the ground can be performed by applying deep learning-based object detectors on drone surveillance footage. The traditional way of hiding military assets from sight is camouflage, for example by using camouflage nets. However, large assets like planes or vessels are difficult to conceal by means of traditional camouflage
Hollander, R.J.M. den +11 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
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Generalized Grad-CAM attacking method based on adversarial patch
Tongxin xuebao, 2021 To verify the fragility of the Grad-CAM, a Grad-CAM attack method based on adversarial patch was proposed.By adding a constraint to the Grad-CAM in the classification loss function, an adversarial patch could be optimized and the adversarial image could ...
Nianwen SI +5 more
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Generative Adversarial Networks for Synthesizing InSAR Patches [PDF]
CoRR, 2020 accepted in preliminary version for EUSAR2020 ...
Sibler, Philipp +4 more
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CoRR, 2017 We present a method to create universal, robust, targeted adversarial image patches in the real world. The patches are universal because they can be used to attack any scene, robust because they work under a wide variety of transformations, and targeted because they can cause a classifier to output any target class.
Tom B. Brown +4 more
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Adversarial patch defense algorithm based on PatchTracker
网络与信息安全学报The application of deep neural networks in target detection has been widely adopted in various fields.However, the introduction of adversarial patch attacks, which add local perturbations to images to mislead deep neural networks, poses a significant ...
Zhenjie XIAO, Shiyu HUANG, Feng YE, Liqing HUANG, Tianqiang HUANG
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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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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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Eigenpatches—Adversarial Patches from Principal Components
, 2023 Adversarial patches are still a simple yet powerful white box attack that can be used to fool object detectors by suppressing possible detections. The patches of these so-called evasion attacks are computational expensive to produce and require full access to the attacked detector.
Jens Bayer +3 more
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