nach oben

Erschienen in:

2021 | OriginalPaper | Buchkapitel

Learning Systems Under Attack—Adversarial Attacks, Defenses and Beyond

verfasst von : Danilo Vasconcellos Vargas

Erschienen in: Autonomous Vehicles

Verlag: Springer Nature Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Deep learning has brought many advances to various fields and enabled applications such as speech and visual recognition to flourish. However, recent findings show that Deep Neural Networks (DNN) still have many problems of their own. The many vulnerabilities present in DNNs unable their application to critical problems. Here, some of these vulnerabilities will be reviewed and many of their possible solutions will be discussed. Regarding legislation, a series of practices will be discussed that could allow for legislation to deal with the increasingly different algorithms available. A small overhead for a safer society. Lastly, as artificial intelligence advances, algorithms should get closer to human beings and legislation itself should face deep philosophical questions in an age in which we will be challenged to reinvent ourselves, as a society and beyond.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Sensors for Automated Driving

Nächstes Kapitel Infrastructure Enabled Autonomy—Autonomy as a Service

Szegedy et al. (2013).

Brown et al. (2017).

Nguyen et al. (2015).

Moosavi-Dezfooli et al. (2017).

Su et al. (2017).

Li et al. (2019).

Vargas and Murata (2017).

Vargas et al. (2013).

Vargas et al. (2015).

Vargas and Su (2019).

Vargas and Kotyan (2019a).

Vargas and Kotyan (2019b).

Brown T, Mané D, Roy A, Abadi M, Gilmer J (2017) Adversarial patch. https://arxiv.org/pdf/1712.09665.pdf. Accessed 30 June 2020

Li D, Vargas DV, Sakurai K (2019) Universal rules for fooling deep neural networks based text classification. In: 2019 IEEE congress on evolutionary computation (CEC), Wellington, New Zealand, June 2019. IEEE, pp 2221–2228

Moosavi-Dezfooli S, Fawzi A, Fawzi O, Frossard P (2017) Universal adversarial perturbations. https://arxiv.org/pdf/1610.08401.pdf. Accessed 30 June 2020

Nguyen A, Yosinski J, Clune J (2015) Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: IEEE conference on computer vision and pattern recognition, Boston, MA, USA, June 2015. IEEE, pp 427–436

Su J, Vargas DV, Sakurai K (2017) One-pixel attack for fooling deep neural networks. https://arxiv.org/pdf/1710.08864.pdf. Accessed 30 June 2020

Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R (2013) Intriguing properties of neural networks. https://arxiv.org/pdf/1312.6199.pdf. Accessed 30 June 2020

Vargas DV, Murata J (2017) Spectrum-diverse neuroevolution with unified neural models. IEEE Trans Neural Netw Learn Syst 28(8):1759–1773CrossRef

Vargas DV, Takano H, Murata J (2013) Self organizing classifiers and niched fitness. In: GECCO ‘13: Proceedings of the 15th annual conference on genetic and evolutionary computation, Amsterdam, Netherlands, July 2013. Association for Computing Machinery, pp 1109–1116

Vargas DV, Takano H, Murata, J (2015) Novelty-organizing team of classifiers in noisy and dynamic environments. In: 2015 IEEE congress on evolutionary computation (CEC), Sendai, Japan, May 2015. IEEE, pp 2937–2944

Vargas DV, Su J (2019) Understanding the one-pixel attack: propagation maps and locality analysis. https://arxiv.org/pdf/1902.02947.pdf. Accessed 30 June 2020

Vargas DV, Kotyan S (2019a) Robustness assessment for adversarial machine learning: problems, solutions and a survey of current models and defenses. https://arxiv.org/pdf/1906.06026.pdf. Accessed 30 June 2020

Vargas DV, Kotyan S (2019b) Evolving robust neural architectures to defend from adversarial attacks. https://arxiv.org/pdf/1906.11667.pdf. Accessed 30 June 2020

Titel: Learning Systems Under Attack—Adversarial Attacks, Defenses and Beyond
verfasst von: Danilo Vasconcellos Vargas
Verlag: Springer Nature Singapore
Buch: Autonomous Vehicles
Print ISBN: 978-981-15-9254-6

Electronic ISBN: 978-981-15-9255-3

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-981-15-9255-3_7

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner