Results 41 to 50 of about 1,662,189 (295)
Are adversarial examples inevitable?
CoRR, 2018 ISBN:978-1-7138-7273 ...
Shafahi, Ali +4 more
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Adversarial Examples for Electrocardiograms
CoRR, 2019 In recent years, the electrocardiogram (ECG) has seen a large diffusion in both medical and commercial applications, fueled by the rise of single-lead versions. Single-lead ECG can be embedded in medical devices and wearable products such as the injectable Medtronic Linq monitor, the iRhythm Ziopatch wearable monitor, and the Apple Watch Series 4 ...
Xintian Han +5 more
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A Multimodal Adversarial Attack Framework Based on Local and Random Search Algorithms
International Journal of Computational Intelligence Systems, 2021 Although many problems in computer vision and natural language processing have made breakthrough progress with neural networks, adversarial attack is a serious potential problem in many neural network- based applications.
Zibo Yi, Jie Yu, Yusong Tan, Qingbo Wu
doaj +1 more source
On the Geometry of Adversarial Examples
CoRR, 2018 Adversarial examples are a pervasive phenomenon of machine learning models where seemingly imperceptible perturbations to the input lead to misclassifications for otherwise statistically accurate models. We propose a geometric framework, drawing on tools from the manifold reconstruction literature, to analyze the high-dimensional geometry of ...
Marc Khoury, Dylan Hadfield-Menell
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Boundary Adversarial Examples Against Adversarial Overfitting
CoRR, 2022 Standard adversarial training approaches suffer from robust overfitting where the robust accuracy decreases when models are adversarially trained for too long. The origin of this problem is still unclear and conflicting explanations have been reported, i.e., memorization effects induced by large loss data or because of small loss data and growing ...
Muhammad Zaid Hameed, Beat Buesser
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Adversarial Examples Generation Method Based on Random Translation Transformation [PDF]
Jisuanji gongcheng, 2022 The image classification model based on Deep Neural Network(DNN) can recognize images with a recognition degree that is even higher than that of human eyes.However, it is vulnerable to attacks from adversarial examples because of the fragility of the ...
LI Zheming, ZHANG Hengwei, MA Junqiang, WANG Jindong, YANG Bo
doaj +1 more source
IEEE Access, 2020 Person re-identification across a network of cameras, with disjoint views, has been studied extensively due to its importance in wide-area video surveillance.
Yu Zheng, Yantao Lu, Senem Velipasalar
doaj +1 more source
IEEE Access, 2022 The internet-of-Vehicle (IoV) can facilitate seamless connectivity between connected vehicles (CV), autonomous vehicles (AV), and other IoV entities. Intrusion Detection Systems (IDSs) for IoV networks can rely on machine learning (ML) to protect the in ...
Ibrahim Aliyu +4 more
doaj +1 more source
Using Single-Step Adversarial Training to Defend Iterative Adversarial Examples
, 2021 Adversarial examples are among the biggest challenges for machine learning models, especially neural network classifiers. Adversarial examples are inputs manipulated with perturbations insignificant to humans while being able to fool machine learning ...
Khreishah, Abdallah +5 more
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Simplicial-Map Neural Networks Robust to Adversarial Examples [PDF]
, 2021 Broadly speaking, an adversarial example against a classification model occurs when a small perturbation on an input data point produces a change on the output label assigned by the model.
Rocio Gonzalez-Diaz +11 more
core +1 more source