Kathrin Grosse | DeepAI

AI Chat

Featured Co-authors

Yang Zhang
141 publications
Mario Fritz
115 publications
Nicolas Papernot
88 publications
Dietrich Klakow
69 publications
Battista Biggio
47 publications
Michael Backes
44 publications
Fabio Roli
43 publications
Marcello Pelillo
39 publications
Patrick McDaniel
38 publications
Mauricio A. Álvarez
37 publications
Ambra Demontis
18 publications

∙ 12/12/2022

A Survey on Reinforcement Learning Security with Application to Autonomous Driving

Reinforcement learning allows machines to learn from their own experienc...

33 Ambra Demontis, et al. ∙

∙ 07/11/2022

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

Despite the large body of academic work on machine learning security, li...

29 Kathrin Grosse, et al. ∙

∙ 06/14/2021

Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions

Backdoor attacks inject poisoning samples during training, with the goal...

5 Antonio Emanuele Cinà, et al. ∙

∙ 05/08/2021

Mental Models of Adversarial Machine Learning

Although machine learning (ML) is widely used in practice, little is kno...

0 Lukas Bieringer, et al. ∙

∙ 07/14/2020

Adversarial Examples and Metrics

Adversarial examples are a type of attack on machine learning (ML) syste...

0 Nico Döttling, et al. ∙

∙ 06/12/2020

How many winning tickets are there in one DNN?

The recent lottery ticket hypothesis proposes that there is one sub-netw...

0 Kathrin Grosse, et al. ∙

∙ 06/11/2020

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

Overfitting describes the phenomenon that a machine learning model fits ...

0 Kathrin Grosse, et al. ∙

∙ 09/19/2019

Adversarial Vulnerability Bounds for Gaussian Process Classification

Machine learning (ML) classification is increasingly used in safety-crit...

6 Michael Thomas Smith, et al. ∙

∙ 02/08/2019

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

The increase in computational power and available data has fueled a wide...

0 Kathrin Grosse, et al. ∙

∙ 12/06/2018

The Limitations of Model Uncertainty in Adversarial Settings

Machine learning models are vulnerable to adversarial examples: minor pe...

0 Kathrin Grosse, et al. ∙

∙ 08/01/2018

MLCapsule: Guarded Offline Deployment of Machine Learning as a Service

With the widespread use of machine learning (ML) techniques, ML as a ser...

0 Lucjan Hanzlik, et al. ∙

∙ 06/06/2018

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

The wide usage of Machine Learning (ML) has lead to research on the atta...

2 Kathrin Grosse, et al. ∙

∙ 11/17/2017

How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models

Machine learning models are vulnerable to adversarial examples: minor, i...

0 Kathrin Grosse, et al. ∙

∙ 02/21/2017

On the (Statistical) Detection of Adversarial Examples

Machine Learning (ML) models are applied in a variety of tasks such as n...

0 Kathrin Grosse, et al. ∙

∙ 06/14/2016

Adversarial Perturbations Against Deep Neural Networks for Malware Classification

Deep neural networks, like many other machine learning models, have rece...

0 Kathrin Grosse, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world