Adversial robustness for machine learning
Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and veri cation. Sections cover adversarial attack, veri cation and defense, mainly focusing on image classi cation applications which are the sta...
Uloženo v:
| Hlavní autoři: | , |
|---|---|
| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
London ; San Diego (Calif.) :
Elsevier : Academic Press
2022.
Paris : Cyberlibris |
| Témata: | |
| On-line přístup: | Accès Université d'Orléans et IFPM |
| Poznámka: |
Couverture. https://static2.cyberlibris.com/books_upload/136pix/9780128242575.jpg Cyberlibris (ScholarVox) corpus Informatique |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Adversial robustness for machine learning, Pin-Yu Chen,... Cho-Jui Hsieh,..., 2023, London, Elsevier, Academic Press, 1 vol. (XIV-283 p.), 978-0-12-824020-5 |
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| 020 | |a 9780128242575 | ||
| 041 | 0 | |a eng | |
| 100 | 1 | |a Chen, Pin-Yu, |d 19..- | |
| 245 | 1 | 0 | |a Adversial robustness for machine learning |c Pin-Yu Chen,... Cho-Jui Hsieh,... |
| 260 | |a London ; |a San Diego (Calif.) : |b Elsevier : |b Academic Press. | ||
| 260 | |a Paris : |b Cyberlibris, |c 2022. | ||
| 500 | |a Couverture. https://static2.cyberlibris.com/books_upload/136pix/9780128242575.jpg | ||
| 500 | |a Cyberlibris (ScholarVox) corpus Informatique | ||
| 504 | |a Bibliogr. p. 251-271. Index | ||
| 506 | |a L'accès en ligne est réservé aux établissements ou bibliothèques ayant souscrit l'abonnement. Cyberlibris | ||
| 520 | |a Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and veri cation. Sections cover adversarial attack, veri cation and defense, mainly focusing on image classi cation applications which are the standard benchmark considered in the adversarial robustness community. Other sections discuss adversarial examples beyond image classification, other threat models beyond testing time attack, and applications on adversarial robustness. For researchers, this book provides a thorough literature review that summarizes latest progress in the area, which can be a good reference for conducting future research. In addition, the book can also be used as a textbook for graduate courses on adversarial robustness or trustworthy machine learning. While machine learning (ML) algorithms have achieved remarkable performance in many applications, recent studies have demonstrated their lack of robustness against adversarial disturbance. The lack of robustness brings security concerns in ML models for real applications such as self-driving cars, robotics controls and healthcare systems | ||
| 650 | |a Apprentissage automatique | ||
| 700 | 1 | |a Hsieh, Cho-Jui, |d 19..- |4 aut | |
| 776 | 0 | |0 272141046 |t Adversial robustness for machine learning |f Pin-Yu Chen,... Cho-Jui Hsieh,... |d 2023 |c London |n Elsevier |n Academic Press |p 1 vol. (XIV-283 p.) |z 978-0-12-824020-5 | |
| 856 | 4 | |5 452349901:845628518 |u https://ezproxy.univ-orleans.fr/login?url=https://univ.scholarvox.com/book/88955559 |z Accès Université d'Orléans et IFPM | |
| 997 | |0 1287123 |1 Livre numérique |a Ressource numérique |b INSA |b ENSA |c 0/Bibliothèque numérique/ |c 1/Bibliothèque numérique/ScholarVox (ebooks)/ | ||