Results 21 to 30 of about 31,109 (263)
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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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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Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks
, 2020 Deep neural networks (DNN) are known to be vulnerable to adversarial attacks. Numerous efforts either try to patch weaknesses in trained models, or try to make it difficult or costly to compute adversarial examples that exploit them.
Li, Bo +5 more
core +1 more source
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
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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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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Harnessing Perceptual Adversarial Patches for Crowd Counting
Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, 2022 Crowd counting, which has been widely adopted for estimating the number of people in safety-critical scenes, is shown to be vulnerable to adversarial examples in the physical world (e.g., adversarial patches). Though harmful, adversarial examples are also valuable for evaluating and better understanding model robustness.
Shunchang Liu +6 more
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