Aprendizado de máquina adversário (Portuguese Wikipedia)

Analysis of information sources in references of the Wikipedia article "Aprendizado de máquina adversário" in Portuguese language version.

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23arxiv.org

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19doi.org

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8worldcat.org

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6unica.it

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4harvard.edu

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4nih.gov

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3jmlr.org

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3ieee.org

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2wired.com

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2towardsdatascience.com

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2springer.com

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1acm.org

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1theverge.com

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1buffalo.edu

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1bbc.com

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1mcafee.com

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1newyorker.com

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1web.archive.org

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1aisnet.org

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1cleverhans.io

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1openai.com

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1microsoft.com

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1purdue.edu

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1iarpa.gov

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1github.com

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acm.org

cacm.acm.org

Goodfellow, Ian; McDaniel, Patrick; Papernot, Nicolas (25 de junho de 2018). «Making machine learning robust against adversarial inputs». Communications of the ACM (em inglês). 61: 56–66. ISSN 0001-0782. doi:10.1145/3134599. Consultado em 13 de dezembro de 2018

aisnet.org

aisel.aisnet.org

Heinrich, Kai; Graf, Johannes; Chen, Ji; Laurisch, Jakob; Zschech, Patrick (15 de junho de 2020). «FOOL ME ONCE, SHAME ON YOU, FOOL ME TWICE, SHAME ON ME: A TAXONOMY OF ATTACK AND DE-FENSE PATTERNS FOR AI SECURITY». ECIS 2020 Research Papers

arxiv.org

Bengio, Samy; Goodfellow, Ian J. (2017). «Adversarial Machine Learning at Scale». arXiv:1611.01236 [cs.CV]
Lim, Hazel Si Min; Taeihagh, Araz (2019). «Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities». Sustainability (em inglês). 11. 5791 páginas. Bibcode:2019arXiv191013122L. arXiv:1910.13122. doi:10.3390/su11205791
Biggio, Battista; Roli, Fabio (dezembro de 2018). «Wild patterns: Ten years after the rise of adversarial machine learning». Pattern Recognition. 84: 317–331. arXiv:1712.03141. doi:10.1016/j.patcog.2018.07.023
Kurakin, Alexey; Goodfellow, Ian. «Adversarial examples in the physical world». arXiv:1607.02533 [cs.CV]
Su, Jiawei; Vargas, Danilo Vasconcellos; Sakurai, Kouichi (2019). «One Pixel Attack for Fooling Deep Neural Networks». IEEE Transactions on Evolutionary Computation. 23: 828–841. arXiv:1710.08864. doi:10.1109/TEVC.2019.2890858
Su, Jiawei; Vargas, Danilo Vasconcellos; Sakurai, Kouichi (outubro de 2019). «One Pixel Attack for Fooling Deep Neural Networks». IEEE Transactions on Evolutionary Computation. 23: 828–841. ISSN 1941-0026. arXiv:1710.08864. doi:10.1109/TEVC.2019.2890858
Athalye, Anish; Engstrom, Logan. «Synthesizing Robust Adversarial Examples». arXiv:1707.07397 [cs.CV]
Zhou, Zhenglong; Firestone, Chaz (2019). «Humans can decipher adversarial images». Nature Communications. 10: 1334. Bibcode:2019NatCo..10.1334Z. PMC 6430776. PMID 30902973. arXiv:1809.04120. doi:10.1038/s41467-019-08931-6
Lepori, Michael A; Firestone, Chaz (27 de março de 2020). «Can you hear me now? Sensitive comparisons of human and machine perception». arXiv:2003.12362 [eess.AS]
Vadillo, Jon; Santana, Roberto (23 de janeiro de 2020). «On the human evaluation of audio adversarial examples». arXiv:2001.08444 [eess.AS]
Biggio, Battista; Corona, Igino; Nelson, Blaine; Rubinstein, Benjamin I. P.; Maiorca, Davide; Fumera (2014). «Security Evaluation of Support Vector Machines in Adversarial Environments». Support Vector Machines Applications (em inglês). [S.l.]: Springer International Publishing. pp. 105–153. ISBN 978-3-319-02300-7. arXiv:1401.7727. doi:10.1007/978-3-319-02300-7_4
Wang, Xinran; Xiang, Yu (13 de setembro de 2020). «Information Laundering for Model Privacy». arXiv:2009.06112 [cs.CR]
Biggio, Battista; Nelson, Blaine (25 de março de 2013). «Poisoning Attacks against Support Vector Machines». arXiv:1206.6389 [cs.LG]
Jagielski, Matthew; Oprea, Alina; Biggio, Battista; Liu, Chang; Nita-Rotaru, Cristina; Li, Bo (maio de 2018). «Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning». IEEE. 2018 IEEE Symposium on Security and Privacy (SP): 19–35. ISBN 978-1-5386-4353-2. arXiv:1804.00308. doi:10.1109/sp.2018.00057
Gu, Tianyu; Dolan-Gavitt, Brendan (11 de março de 2019). «BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain». arXiv:1708.06733 [cs.CR]
Veale, Michael; Binns, Reuben; Edwards, Lilian (28 de novembro de 2018). «Algorithms that remember: model inversion attacks and data protection law». Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences. 376. Bibcode:2018RSPTA.37680083V. ISSN 1364-503X. PMC 6191664. PMID 30322998. arXiv:1807.04644. doi:10.1098/rsta.2018.0083
Shokri, Reza; Stronati, Marco (31 de março de 2017). «Membership Inference Attacks against Machine Learning Models». arXiv:1610.05820 [cs.CR]
Goodfellow, Ian J.; Shlens, Jonathon (20 de março de 2015). «Explaining and Harnessing Adversarial Examples». arXiv:1412.6572 [stat.ML]
Madry, Aleksander; Makelov, Aleksandar (4 de setembro de 2019). «Towards Deep Learning Models Resistant to Adversarial Attacks». arXiv:1706.06083 [stat.ML]
Carlini, Nicholas; Wagner, David (22 de março de 2017). «Towards Evaluating the Robustness of Neural Networks». arXiv:1608.04644 [cs.CR]
Brown, Tom B.; Mané, Dandelion (16 de maio de 2018). «Adversarial Patch». arXiv:1712.09665 [cs.CV]
Kishor Datta Gupta; Akhtar, Zahid. «Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial Attacks». arXiv:2007.00337 [cs.CV]
B. I. P. Rubinstein, P. L. Bartlett, L. Huang, and N. Taft. "Learning in a large function space: Privacy- preserving mechanisms for svm learning". Journal of Privacy and Confidentiality, 4(1):65–100, 2012.

bbc.com

«Single pixel change fools AI programs». BBC News. 3 de novembro de 2017. Consultado em 12 de fevereiro de 2018

buffalo.edu

cubs.cedar.buffalo.edu

Rodrigues, Ricardo N.; Ling, Lee Luan; Govindaraju, Venu (1 de junho de 2009). «Robustness of multimodal biometric fusion methods against spoof attacks» (PDF). Journal of Visual Languages & Computing (em inglês). 20: 169–179. ISSN 1045-926X. doi:10.1016/j.jvlc.2009.01.010

cleverhans.io

«How to steal modern NLP systems with gibberish?». cleverhans-blog (em inglês). 6 de abril de 2020. Consultado em 15 de outubro de 2020

doi.org

dx.doi.org

Kianpour, Mazaher; Wen, Shao-Fang (2020). «Timing Attacks on Machine Learning: State of the Art». Intelligent Systems and Applications. Col: Advances in Intelligent Systems and Computing (em inglês). 1037. [S.l.: s.n.] pp. 111–125. ISBN 978-3-030-29515-8. doi:10.1007/978-3-030-29516-5_10
Lim, Hazel Si Min; Taeihagh, Araz (2019). «Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities». Sustainability (em inglês). 11. 5791 páginas. Bibcode:2019arXiv191013122L. arXiv:1910.13122. doi:10.3390/su11205791
Goodfellow, Ian; McDaniel, Patrick; Papernot, Nicolas (25 de junho de 2018). «Making machine learning robust against adversarial inputs». Communications of the ACM (em inglês). 61: 56–66. ISSN 0001-0782. doi:10.1145/3134599. Consultado em 13 de dezembro de 2018
Biggio, Battista; Roli, Fabio (dezembro de 2018). «Wild patterns: Ten years after the rise of adversarial machine learning». Pattern Recognition. 84: 317–331. arXiv:1712.03141. doi:10.1016/j.patcog.2018.07.023
Biggio, Battista; Fumera, Giorgio; Roli, Fabio (2010). «Multiple classifier systems for robust classifier design in adversarial environments». International Journal of Machine Learning and Cybernetics. 1: 27–41. ISSN 1868-8071. doi:10.1007/s13042-010-0007-7
Rodrigues, Ricardo N.; Ling, Lee Luan; Govindaraju, Venu (1 de junho de 2009). «Robustness of multimodal biometric fusion methods against spoof attacks» (PDF). Journal of Visual Languages & Computing (em inglês). 20: 169–179. ISSN 1045-926X. doi:10.1016/j.jvlc.2009.01.010
Su, Jiawei; Vargas, Danilo Vasconcellos; Sakurai, Kouichi (2019). «One Pixel Attack for Fooling Deep Neural Networks». IEEE Transactions on Evolutionary Computation. 23: 828–841. arXiv:1710.08864. doi:10.1109/TEVC.2019.2890858
Su, Jiawei; Vargas, Danilo Vasconcellos; Sakurai, Kouichi (outubro de 2019). «One Pixel Attack for Fooling Deep Neural Networks». IEEE Transactions on Evolutionary Computation. 23: 828–841. ISSN 1941-0026. arXiv:1710.08864. doi:10.1109/TEVC.2019.2890858
Zhou, Zhenglong; Firestone, Chaz (2019). «Humans can decipher adversarial images». Nature Communications. 10: 1334. Bibcode:2019NatCo..10.1334Z. PMC 6430776. PMID 30902973. arXiv:1809.04120. doi:10.1038/s41467-019-08931-6
Heaven, Douglas (outubro de 2019). «Why deep-learning AIs are so easy to fool». Nature (em inglês). 574: 163–166. Bibcode:2019Natur.574..163H. PMID 31597977. doi:10.1038/d41586-019-03013-5
Hutson, Matthew (10 de maio de 2019). «AI can now defend itself against malicious messages hidden in speech». Nature. PMID 32385365. doi:10.1038/d41586-019-01510-1
Barreno, Marco; Nelson, Blaine; Joseph, Anthony D.; Tygar, J. D. (2010). «The security of machine learning» (PDF). Machine Learning. 81: 121–148. doi:10.1007/s10994-010-5188-5
Sikos, Leslie F. (2019). AI in Cybersecurity. Col: Intelligent Systems Reference Library. 151. Cham: Springer. ISBN 978-3-319-98841-2. doi:10.1007/978-3-319-98842-9
Biggio, Battista; Corona, Igino; Nelson, Blaine; Rubinstein, Benjamin I. P.; Maiorca, Davide; Fumera (2014). «Security Evaluation of Support Vector Machines in Adversarial Environments». Support Vector Machines Applications (em inglês). [S.l.]: Springer International Publishing. pp. 105–153. ISBN 978-3-319-02300-7. arXiv:1401.7727. doi:10.1007/978-3-319-02300-7_4
Jagielski, Matthew; Oprea, Alina; Biggio, Battista; Liu, Chang; Nita-Rotaru, Cristina; Li, Bo (maio de 2018). «Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning». IEEE. 2018 IEEE Symposium on Security and Privacy (SP): 19–35. ISBN 978-1-5386-4353-2. arXiv:1804.00308. doi:10.1109/sp.2018.00057
Veale, Michael; Binns, Reuben; Edwards, Lilian (28 de novembro de 2018). «Algorithms that remember: model inversion attacks and data protection law». Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences. 376. Bibcode:2018RSPTA.37680083V. ISSN 1364-503X. PMC 6191664. PMID 30322998. arXiv:1807.04644. doi:10.1098/rsta.2018.0083
Liu, Wei; Chawla, Sanjay (2010). «Mining adversarial patterns via regularized loss minimization» (PDF). Machine Learning. 81: 69–83. doi:10.1007/s10994-010-5199-2
Chivukula, Aneesh; Yang, Xinghao; Liu, Wei; Zhu, Tianqing; Zhou, Wanlei (2020). «Game Theoretical Adversarial Deep Learning with Variational Adversaries». IEEE Transactions on Knowledge and Data Engineering. 1 páginas. ISSN 1558-2191. doi:10.1109/TKDE.2020.2972320
Chivukula, Aneesh Sreevallabh; Liu, Wei (2019). «Adversarial Deep Learning Models with Multiple Adversaries». IEEE Transactions on Knowledge and Data Engineering. 31: 1066–1079. ISSN 1558-2191. doi:10.1109/TKDE.2018.2851247

github.com

«cchio/deep-pwning». GitHub. Consultado em 8 de agosto de 2016

harvard.edu

ui.adsabs.harvard.edu

Lim, Hazel Si Min; Taeihagh, Araz (2019). «Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities». Sustainability (em inglês). 11. 5791 páginas. Bibcode:2019arXiv191013122L. arXiv:1910.13122. doi:10.3390/su11205791
Zhou, Zhenglong; Firestone, Chaz (2019). «Humans can decipher adversarial images». Nature Communications. 10: 1334. Bibcode:2019NatCo..10.1334Z. PMC 6430776. PMID 30902973. arXiv:1809.04120. doi:10.1038/s41467-019-08931-6
Heaven, Douglas (outubro de 2019). «Why deep-learning AIs are so easy to fool». Nature (em inglês). 574: 163–166. Bibcode:2019Natur.574..163H. PMID 31597977. doi:10.1038/d41586-019-03013-5
Veale, Michael; Binns, Reuben; Edwards, Lilian (28 de novembro de 2018). «Algorithms that remember: model inversion attacks and data protection law». Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences. 376. Bibcode:2018RSPTA.37680083V. ISSN 1364-503X. PMC 6191664. PMID 30322998. arXiv:1807.04644. doi:10.1098/rsta.2018.0083

iarpa.gov

«TrojAI». www.iarpa.gov. Consultado em 14 de outubro de 2020

ieee.org

ieeexplore.ieee.org

Chivukula, Aneesh; Yang, Xinghao; Liu, Wei; Zhu, Tianqing; Zhou, Wanlei (2020). «Game Theoretical Adversarial Deep Learning with Variational Adversaries». IEEE Transactions on Knowledge and Data Engineering. 1 páginas. ISSN 1558-2191. doi:10.1109/TKDE.2020.2972320
Chivukula, Aneesh Sreevallabh; Liu, Wei (2019). «Adversarial Deep Learning Models with Multiple Adversaries». IEEE Transactions on Knowledge and Data Engineering. 31: 1066–1079. ISSN 1558-2191. doi:10.1109/TKDE.2018.2851247

spectrum.ieee.org

Ackerman, Evan (4 de agosto de 2017). «Slight Street Sign Modifications Can Completely Fool Machine Learning Algorithms». IEEE Spectrum: Technology, Engineering, and Science News (em inglês). Consultado em 15 de julho de 2019

jmlr.org

Brückner, Michael; Kanzow, Christian; Scheffer, Tobias (2012). «Static Prediction Games for Adversarial Learning Problems» (PDF). Journal of Machine Learning Research. 13: 2617–2654. ISSN 1533-7928
B. Nelson, B. I. Rubinstein, L. Huang, A. D. Joseph, S. J. Lee, S. Rao, and J. D. Tygar. "Query strategies for evading convex-inducing classifiers". J. Mach. Learn. Res., 13:1293–1332, 2012
M. Kloft and P. Laskov. "Security analysis of online centroid anomaly detection". Journal of Machine Learning Research, 13:3647–3690, 2012.

mcafee.com

securingtomorrow.mcafee.com

«Model Hacking ADAS to Pave Safer Roads for Autonomous Vehicles». McAfee Blogs (em inglês). 19 de fevereiro de 2020. Consultado em 11 de março de 2020

microsoft.com

O. Dekel, O. Shamir, and L. Xiao. "Learning to classify with missing and corrupted features". Machine Learning, 81:149–178, 2010.

newyorker.com

Seabrook, John (2020). «Dressing for the Surveillance Age». The New Yorker (em inglês). Consultado em 5 de abril de 2020

nih.gov

ncbi.nlm.nih.gov

Zhou, Zhenglong; Firestone, Chaz (2019). «Humans can decipher adversarial images». Nature Communications. 10: 1334. Bibcode:2019NatCo..10.1334Z. PMC 6430776. PMID 30902973. arXiv:1809.04120. doi:10.1038/s41467-019-08931-6
Heaven, Douglas (outubro de 2019). «Why deep-learning AIs are so easy to fool». Nature (em inglês). 574: 163–166. Bibcode:2019Natur.574..163H. PMID 31597977. doi:10.1038/d41586-019-03013-5
Hutson, Matthew (10 de maio de 2019). «AI can now defend itself against malicious messages hidden in speech». Nature. PMID 32385365. doi:10.1038/d41586-019-01510-1
Veale, Michael; Binns, Reuben; Edwards, Lilian (28 de novembro de 2018). «Algorithms that remember: model inversion attacks and data protection law». Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences. 376. Bibcode:2018RSPTA.37680083V. ISSN 1364-503X. PMC 6191664. PMID 30322998. arXiv:1807.04644. doi:10.1098/rsta.2018.0083

openai.com

«Attacking Machine Learning with Adversarial Examples». OpenAI (em inglês). 24 de fevereiro de 2017. Consultado em 15 de outubro de 2020

purdue.edu

stat.purdue.edu

M. Kantarcioglu, B. Xi, C. Clifton. "Classifier Evaluation and Attribute Selection against Active Adversaries". Data Min. Knowl. Discov., 22:291–335, January 2011.

springer.com

link.springer.com

Barreno, Marco; Nelson, Blaine; Joseph, Anthony D.; Tygar, J. D. (2010). «The security of machine learning» (PDF). Machine Learning. 81: 121–148. doi:10.1007/s10994-010-5188-5
Liu, Wei; Chawla, Sanjay (2010). «Mining adversarial patterns via regularized loss minimization» (PDF). Machine Learning. 81: 69–83. doi:10.1007/s10994-010-5199-2

theverge.com

Vincent, James (12 de abril de 2017). «Magic AI: these are the optical illusions that trick, fool, and flummox computers». The Verge (em inglês). Consultado em 27 de março de 2020

towardsdatascience.com

Jain, Anant (9 de fevereiro de 2019). «Breaking neural networks with adversarial attacks - Towards Data Science». Medium (em inglês). Consultado em 15 de julho de 2019
Moisejevs, Ilja (15 de julho de 2019). «Poisoning attacks on Machine Learning - Towards Data Science». Medium (em inglês). Consultado em 15 de julho de 2019

unica.it

pralab.diee.unica.it

Biggio, Battista; Fumera, Giorgio; Roli, Fabio (2010). «Multiple classifier systems for robust classifier design in adversarial environments». International Journal of Machine Learning and Cybernetics. 1: 27–41. ISSN 1868-8071. doi:10.1007/s13042-010-0007-7
B. Biggio, G. Fumera, and F. Roli. "Pattern recognition systems under attack: Design issues and research challenges". Int'l J. Patt. Recogn. Artif. Intell., 28(7):1460002, 2014.
B. Biggio, G. Fumera, and F. Roli. "Security evaluation of pattern classifiers under attack Arquivado em 2018-05-18 no Wayback Machine". IEEE Transactions on Knowledge and Data Engineering, 26(4):984–996, 2014.
B. Biggio, B. Nelson, and P. Laskov. "Support vector machines under adversarial label noise". In Journal of Machine Learning Research - Proc. 3rd Asian Conf. Machine Learning, volume 20, pp. 97–112, 2011.
B. Biggio, G. Fumera, and F. Roli. "Evade hard multiple classifier systems". In O. Okun and G. Valentini, editors, Supervised and Unsupervised Ensemble Methods and Their Applications, volume 245 of Studies in Computational Intelligence, pages 15–38. Springer Berlin / Heidelberg, 2009.
H. Xiao, B. Biggio, B. Nelson, H. Xiao, C. Eckert, and F. Roli. "Support vector machines under adversarial label contamination". Neurocomputing, Special Issue on Advances in Learning with Label Noise, In Press.

web.archive.org

B. Biggio, G. Fumera, and F. Roli. "Security evaluation of pattern classifiers under attack Arquivado em 2018-05-18 no Wayback Machine". IEEE Transactions on Knowledge and Data Engineering, 26(4):984–996, 2014.

wired.com

«AI Has a Hallucination Problem That's Proving Tough to Fix». WIRED. 2018. Consultado em 10 de março de 2018
«A Tiny Piece of Tape Tricked Teslas Into Speeding Up 50 MPH». Wired (em inglês). 2020. Consultado em 11 de março de 2020

worldcat.org

Goodfellow, Ian; McDaniel, Patrick; Papernot, Nicolas (25 de junho de 2018). «Making machine learning robust against adversarial inputs». Communications of the ACM (em inglês). 61: 56–66. ISSN 0001-0782. doi:10.1145/3134599. Consultado em 13 de dezembro de 2018
Biggio, Battista; Fumera, Giorgio; Roli, Fabio (2010). «Multiple classifier systems for robust classifier design in adversarial environments». International Journal of Machine Learning and Cybernetics. 1: 27–41. ISSN 1868-8071. doi:10.1007/s13042-010-0007-7
Brückner, Michael; Kanzow, Christian; Scheffer, Tobias (2012). «Static Prediction Games for Adversarial Learning Problems» (PDF). Journal of Machine Learning Research. 13: 2617–2654. ISSN 1533-7928
Rodrigues, Ricardo N.; Ling, Lee Luan; Govindaraju, Venu (1 de junho de 2009). «Robustness of multimodal biometric fusion methods against spoof attacks» (PDF). Journal of Visual Languages & Computing (em inglês). 20: 169–179. ISSN 1045-926X. doi:10.1016/j.jvlc.2009.01.010
Su, Jiawei; Vargas, Danilo Vasconcellos; Sakurai, Kouichi (outubro de 2019). «One Pixel Attack for Fooling Deep Neural Networks». IEEE Transactions on Evolutionary Computation. 23: 828–841. ISSN 1941-0026. arXiv:1710.08864. doi:10.1109/TEVC.2019.2890858
Veale, Michael; Binns, Reuben; Edwards, Lilian (28 de novembro de 2018). «Algorithms that remember: model inversion attacks and data protection law». Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences. 376. Bibcode:2018RSPTA.37680083V. ISSN 1364-503X. PMC 6191664. PMID 30322998. arXiv:1807.04644. doi:10.1098/rsta.2018.0083
Chivukula, Aneesh; Yang, Xinghao; Liu, Wei; Zhu, Tianqing; Zhou, Wanlei (2020). «Game Theoretical Adversarial Deep Learning with Variational Adversaries». IEEE Transactions on Knowledge and Data Engineering. 1 páginas. ISSN 1558-2191. doi:10.1109/TKDE.2020.2972320
Chivukula, Aneesh Sreevallabh; Liu, Wei (2019). «Adversarial Deep Learning Models with Multiple Adversaries». IEEE Transactions on Knowledge and Data Engineering. 31: 1066–1079. ISSN 1558-2191. doi:10.1109/TKDE.2018.2851247