Kathrin Grosse

Giovanni Apruzzese

Proceedings of the 57th Hawaii International Conference on System Sciences, 2024

When Your AI Becomes a Target: AI Security Incidents and Best Practices.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Adversarial vulnerability bounds for Gaussian process classification.

[BibT_eX]

[DOI]

Mach. Learn., March, 2023

Machine Learning Security in Industry: A Quantitative Survey.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Forensics Secur., 2023

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning.

[BibT_eX]

[DOI]

Antonio Emanuele Cinà

ACM Comput. Surv., 2023

Manipulating Trajectory Prediction with Backdoors.

[BibT_eX]

[DOI]

CoRR, 2023

Towards more Practical Threat Models in Artificial Intelligence Security.

[BibT_eX]

[DOI]

CoRR, 2023

2022

A Survey on Reinforcement Learning Security with Application to Autonomous Driving.

[BibT_eX]

[DOI]

CoRR, 2022

"Why do so?" - A Practical Perspective on Machine Learning Security.

[BibT_eX]

[DOI]

CoRR, 2022

Backdoor smoothing: Demystifying backdoor attacks on deep neural networks.

[BibT_eX]

[DOI]

Comput. Secur., 2022

Industrial practitioners' mental models of adversarial machine learning.

[BibT_eX]

[DOI]

Proceedings of the Eighteenth Symposium on Usable Privacy and Security, 2022

2021

Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions.

[BibT_eX]

[DOI]

Antonio Emanuele Cinà

CoRR, 2021

Mental Models of Adversarial Machine Learning.

[BibT_eX]

[DOI]

CoRR, 2021

Do winning tickets exist before DNN training?

[BibT_eX]

[DOI]

Proceedings of the 2021 SIAM International Conference on Data Mining, 2021

MLCapsule: Guarded Offline Deployment of Machine Learning as a Service.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020

Why is Machine Learning Security so hard?

[BibT_eX]

[DOI]

PhD thesis, 2020

Adversarial Examples and Metrics.

[BibT_eX]

[DOI]

CoRR, 2020

How many winning tickets are there in one DNN?

[BibT_eX]

[DOI]

CoRR, 2020

A new measure for overfitting and its implications for backdooring of deep learning.

[BibT_eX]

[DOI]

CoRR, 2020

Killing Four Birds with one Gaussian Process: The Relation between different Test-Time Attacks.

[BibT_eX]

[DOI]

Michael T. Smith

Proceedings of the 25th International Conference on Pattern Recognition, 2020

On the Security Relevance of Initial Weights in Deep Neural Networks.

[BibT_eX]

[DOI]

Thomas Alexander Trost

Marius Mosbach

Dietrich Klakow

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2020, 2020

2019

Adversarial Initialization - when your network performs the way I want.

[BibT_eX]

[DOI]

Thomas Alexander Trost

Marius Mosbach

Dietrich Klakow

CoRR, 2019

2018

The Limitations of Model Uncertainty in Adversarial Settings.

[BibT_eX]

[DOI]

CoRR, 2018

Killing Three Birds with one Gaussian Process: Analyzing Attack Vectors on Classification.

[BibT_eX]

[DOI]

Michael T. Smith

CoRR, 2018

2017

On the (Statistical) Detection of Adversarial Examples.

[BibT_eX]

[DOI]

CoRR, 2017

Adversarial Examples for Malware Detection.

[BibT_eX]

[DOI]

Proceedings of the Computer Security - ESORICS 2017, 2017

2016

Adversarial Perturbations Against Deep Neural Networks for Malware Classification.

[BibT_eX]

[DOI]

CoRR, 2016

2015

Integrating argumentation and sentiment analysis for mining opinions from Twitter.

[BibT_eX]

[DOI]

María Paula González

AI Commun., 2015

2013

A First Approach to Mining Opinions as Multisets through Argumentation.

[BibT_eX]

[DOI]

María Paula González

Proceedings of the Agreement Technologies - Second International Conference, 2013

2012

Empowering an E-Government Platform Through Twitter-Based Arguments.

[BibT_eX]

[DOI]

Elsa Estevez

Inteligencia Artif., 2012

An Argument-based Approach to Mining Opinions from Twitter.

[BibT_eX]

[DOI]