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Jere et al., 2020 - Google Patents

Principal component properties of adversarial samples

Jere et al., 2020

View PDF
Document ID
3207109391228094888
Author
Jere M
Herbig S
Lind C
Koushanfar F
Publication year
Publication venue
Engineering Dependable and Secure Machine Learning Systems: Third International Workshop, EDSMLS 2020, New York City, NY, USA, February 7, 2020, Revised Selected Papers 3

External Links

Snippet

Abstract Deep Neural Networks for image classification have been found to be vulnerable to adversarial samples, which consist of sub-perceptual noise added to a benign image that can easily fool trained neural networks, posing a significant risk to their commercial …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6228—Selecting the most significant subset of features
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    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
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