A Marauder's Map of Security and Privacy in Machine Learning: An overview of current and future research directions for making machine learning secure and private.

[BibT_eX]

[DOI]

Nicolas Papernot

Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security, 2018

Detection under Privileged Information.

[BibT_eX]

[DOI]

Proceedings of the 2018 on Asia Conference on Computer and Communications Security, 2018

2017

On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches.

[BibT_eX]

[DOI]

CoRR, 2017

The Space of Transferable Adversarial Examples.

[BibT_eX]

[DOI]

CoRR, 2017

Ensemble Adversarial Training: Attacks and Defenses.

[BibT_eX]

[DOI]

CoRR, 2017

Extending Defensive Distillation.

[BibT_eX]

[DOI]

Nicolas Papernot

Patrick D. McDaniel

CoRR, 2017

On the (Statistical) Detection of Adversarial Examples.

[BibT_eX]

[DOI]

CoRR, 2017

Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data.

[BibT_eX]

[DOI]

Proceedings of the 5th International Conference on Learning Representations, 2017

Adversarial Attacks on Neural Network Policies.

[BibT_eX]

[DOI]

Proceedings of the 5th International Conference on Learning Representations, 2017

Adversarial Examples for Malware Detection.

[BibT_eX]

[DOI]

Proceedings of the Computer Security - ESORICS 2017, 2017

On the Protection of Private Information in Machine Learning Systems: Two Recent Approches.

[BibT_eX]

[DOI]

Proceedings of the 30th IEEE Computer Security Foundations Symposium, 2017

Practical Black-Box Attacks against Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, 2017

2016

Machine Learning in Adversarial Settings.

[BibT_eX]

[DOI]

Patrick D. McDaniel

Nicolas Papernot

Z. Berkay Celik

IEEE Secur. Priv., 2016

Towards the Science of Security and Privacy in Machine Learning.

[BibT_eX]

[DOI]

CoRR, 2016

Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples.

[BibT_eX]

[DOI]

CoRR, 2016

Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples.

[BibT_eX]

[DOI]

Nicolas Papernot

Patrick D. McDaniel

Ian J. Goodfellow

CoRR, 2016

On the Effectiveness of Defensive Distillation.

[BibT_eX]

[DOI]

Nicolas Papernot

Patrick D. McDaniel

CoRR, 2016

Adversarial Perturbations Against Deep Neural Networks for Malware Classification.

[BibT_eX]

[DOI]

CoRR, 2016

cleverhans v0.1: an adversarial machine learning library.

[BibT_eX]

[DOI]

Ian J. Goodfellow

Nicolas Papernot

Patrick D. McDaniel

CoRR, 2016

Building Better Detection with Privileged Information.

[BibT_eX]

[DOI]

CoRR, 2016

Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium on Security and Privacy, 2016

Crafting adversarial input sequences for recurrent neural networks.

[BibT_eX]

[DOI]

Proceedings of the 2016 IEEE Military Communications Conference, 2016

Mapping sample scenarios to operational models.

[BibT_eX]

[DOI]

Proceedings of the 2016 IEEE Military Communications Conference, 2016

The Limitations of Deep Learning in Adversarial Settings.

[BibT_eX]

[DOI]

Proceedings of the IEEE European Symposium on Security and Privacy, 2016

2015

Enforcing agile access control policies in relational databases using views.

[BibT_eX]

[DOI]

Nicolas Papernot

Patrick D. McDaniel

Robert J. Walls

Proceedings of the 34th IEEE Military Communications Conference, 2015

2014

Security and Science of Agility.

[BibT_eX]

[DOI]

Srikanth V. Krishnamurthy

Iulian Neamtiu

Proceedings of the First ACM Workshop on Moving Target Defense, 2014

Nicolas Papernot

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