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Adversarial Examples in Arabic
2019 International Conference on Computational Science and Computational Intelligence (CSCI), 2019Several studies have shown that deep neural networks (DNNs) are vulnerable to adversarial examples - perturbed inputs that cause DNN-based models to produce incorrect outputs. A variety of adversarial attacks have been proposed in the domains of computer vision and natural language processing (NLP); however, most attacks in the NLP domain have been ...
Basemah Alshemali, Jugal Kalita
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Revealing Perceptual Proxies with Adversarial Examples
IEEE Transactions on Visualization and Computer Graphics, 2021Data visualizations convert numbers into visual marks so that our visual system can extract data from an image instead of raw numbers. Clearly, the visual system does not compute these values as a computer would, as an arithmetic mean or a correlation. Instead, it extracts these patterns using perceptual proxies; heuristic shortcuts of the visual marks,
Brian D, Ondov +4 more
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Adversarial Examples for Hamming Space Search
IEEE Transactions on Cybernetics, 2020Due to its strong representation learning ability and its facilitation of joint learning for representation and hash codes, deep learning-to-hash has achieved promising results and is becoming increasingly popular for the large-scale approximate nearest neighbor search.
Erkun Yang +3 more
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Advops: Decoupling Adversarial Examples
Pattern Recognition, 2023Donghua Wang +3 more
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Rethinking Adversarial Examples
Traditionally, adversarial examples have been defined as imperceptible perturbations that fool deep neural networks. This thesis challenges this view by examining unrestricted adversarial examples – a broader class of manipulations that can compromise model security while preserving semantics.openaire +1 more source
Adversarial examples: A survey
2018 Baltic URSI Symposium (URSI), 2018Adversarial examples are a phenomenon that have gathered a lot of attention in recent studies. The fact that the addition of very small, but carefully crafted perturbations to the inputs of sophisticated and high performing machine learning models may cause them to make significant errors, is both fascinating and important.
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Bio-based polymers with performance-advantaged properties
Nature Reviews Materials, 2021Robin M Cywar +2 more
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Electrochemical ion insertion from the atomic to the device scale
Nature Reviews Materials, 2021Aditya Sood +2 more
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The electrical conductivity of solution-processed nanosheet networks
Nature Reviews Materials, 2021Adam G Kelly +2 more
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Improving cancer immunotherapy using nanomedicines: progress, opportunities and challenges
Nature Reviews Clinical Oncology, 2020John D Martin +2 more
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