Results 31 to 40 of about 619 (184)
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
openaire +2 more sources
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
doaj +2 more sources
Hindering Adversarial Attacks with Multiple Encrypted Patch Embeddings
2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2023 To appear in APSIPA ASC ...
AprilPyone MaungMaung +2 more
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Remote Sensing, 2023 Deep learning (DL) models have recently been widely used in UAV aerial image semantic segmentation tasks and have achieved excellent performance. However, DL models are vulnerable to adversarial examples, which bring significant security risks to safety ...
Zhen Wang +3 more
doaj +1 more source
Adversarial Attacks on Image Classification Models: FGSM and Patch Attacks and Their Impact
, 2023 This chapter introduces the concept of adversarial attacks on image classification models built on convolutional neural networks (CNN). CNNs are very popular deep-learning models which are used in image classification tasks. However, very powerful and pre-trained CNN models working very accurately on image datasets for image classification tasks may ...
Jaydip Sen, Subhasis Dasgupta
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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
doaj +1 more source
Care and COVID 19: Lessons for liberals and neoliberals
Child &Family Social Work, EarlyView., 2023 Abstract Within the liberal political traditions, care is regarded as a private matter, a problem of ethics rather than justice. Social justice is framed as an issue of economics (re/distribution), culture (recognition) and/or politics (representation).
Kathleen Lynch
wiley +1 more source
Camouflaged Adversarial Patch Attack on Object Detector
Journal of the Korea Institute of Military Science and Technology, 2023 Adversarial attacks have received great attentions for their capacity to distract state-of-the-art neural networks by modifying objects in physical domain. Patch-based attack especially have got much attention for its optimization effectiveness and feasible adaptation to any objects to attack neural network-based object detectors.
Jeonghun Kim, Hunmin Yang, Se-Yoon Oh
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Suppress with a Patch: Revisiting Universal Adversarial Patch Attacks against Object Detection
2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2022 Adversarial patch-based attacks aim to fool a neural network with an intentionally generated noise, which is concentrated in a particular region of an input image. In this work, we perform an in-depth analysis of different patch generation parameters, including initialization, patch size, and especially positioning a patch in an image during training ...
Pavlitskaya, Svetlana +5 more
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Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches
, 2022 Deep learning has substantially boosted the performance of Monocular Depth Estimation (MDE), a critical component in fully vision-based autonomous driving (AD) systems (e.g., Tesla and Toyota). In this work, we develop an attack against learning-based MDE.
Zhiyuan Cheng 0010 +6 more
openaire +2 more sources