Nicolas Papernot

According to our database1, Nicolas Papernot authored at least 34 papers between 2014 and 2018.

Collaborative distances:

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Other 

Links

On csauthors.net:

Bibliography

2018
Adversarial Vision Challenge.
CoRR, 2018

Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning.
CoRR, 2018

Scalable Private Learning with PATE.
CoRR, 2018

Adversarial Examples that Fool both Human and Computer Vision.
CoRR, 2018

Making machine learning robust against adversarial inputs.
Commun. ACM, 2018

SoK: Security and Privacy in Machine Learning.
Proceedings of the 2018 IEEE European Symposium on Security and Privacy, 2018

Detection under Privileged Information.
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.
CoRR, 2017

The Space of Transferable Adversarial Examples.
CoRR, 2017

Ensemble Adversarial Training: Attacks and Defenses.
CoRR, 2017

Extending Defensive Distillation.
CoRR, 2017

Adversarial Attacks on Neural Network Policies.
CoRR, 2017

On the (Statistical) Detection of Adversarial Examples.
CoRR, 2017

Adversarial Examples for Malware Detection.
Proceedings of the Computer Security - ESORICS 2017, 2017

On the Protection of Private Information in Machine Learning Systems: Two Recent Approches.
Proceedings of the 30th IEEE Computer Security Foundations Symposium, 2017

Practical Black-Box Attacks against Machine Learning.
Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, 2017

2016
Machine Learning in Adversarial Settings.
IEEE Security & Privacy, 2016

Towards the Science of Security and Privacy in Machine Learning.
CoRR, 2016

Crafting Adversarial Input Sequences for Recurrent Neural Networks.
CoRR, 2016

Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples.
CoRR, 2016

Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples.
CoRR, 2016

On the Effectiveness of Defensive Distillation.
CoRR, 2016

Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data.
CoRR, 2016

Adversarial Perturbations Against Deep Neural Networks for Malware Classification.
CoRR, 2016

cleverhans v0.1: an adversarial machine learning library.
CoRR, 2016

Building Better Detection with Privileged Information.
CoRR, 2016

Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks.
Proceedings of the IEEE Symposium on Security and Privacy, 2016

Crafting adversarial input sequences for recurrent neural networks.
Proceedings of the 2016 IEEE Military Communications Conference, 2016

Mapping sample scenarios to operational models.
Proceedings of the 2016 IEEE Military Communications Conference, 2016

The Limitations of Deep Learning in Adversarial Settings.
Proceedings of the IEEE European Symposium on Security and Privacy, 2016

2015
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks.
CoRR, 2015

The Limitations of Deep Learning in Adversarial Settings.
CoRR, 2015

Enforcing agile access control policies in relational databases using views.
Proceedings of the 34th IEEE Military Communications Conference, 2015

2014
Security and Science of Agility.
Proceedings of the First ACM Workshop on Moving Target Defense, 2014


  Loading...