Kathrin Grosse

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 35

Top Topics

Gaussian Process

Learning Curves

Machine Learning

Top Venues

SOUPS @ USENIX Security Symposium

Publications

Antonio Emanuele Cinà, Kathrin Grosse, Ambra Demontis, Battista Biggio, Fabio Roli, Marcello Pelillo
Machine Learning Security Against Data Poisoning: Are We There Yet? Computer 57 (3) (2024)
Fiona Koh, Kathrin Grosse, Giovanni Apruzzese
Voices from the Frontline: Revealing the AI Practitioners' viewpoint on the European AI Act. HICSS (2024)
Hamid Eghbal-zadeh, Werner Zellinger, Maura Pintor, Kathrin Grosse, Khaled Koutini, Bernhard Alois Moser, Battista Biggio, Gerhard Widmer
Rethinking data augmentation for adversarial robustness. Inf. Sci. 654 (2024)
David Fernández Llorca, Ronan Hamon, Henrik Junklewitz, Kathrin Grosse, Lars Kunze, Patrick Seiniger, Robert Swaim, Nick Reed, Alexandre Alahi, Emilia Gómez, Ignacio Sánchez, Ákos Kriston
Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness. CoRR (2024)
Kathrin Grosse, Lukas Bieringer, Tarek R. Besold, Battista Biggio, Alexandre Alahi
When Your AI Becomes a Target: AI Security Incidents and Best Practices. AAAI (2024)
Kathrin Grosse, Lukas Bieringer, Tarek R. Besold, Alexandre Alahi
Towards More Practical Threat Models in Artificial Intelligence Security. USENIX Security Symposium (2024)
Kaouther Messaoud, Kathrin Grosse, Mickaël Chen, Matthieu Cord, Patrick Pérez, Alexandre Alahi
Manipulating Trajectory Prediction with Backdoors. CoRR (2023)
Kathrin Grosse, Lukas Bieringer, Tarek R. Besold, Battista Biggio, Katharina Krombholz
Machine Learning Security in Industry: A Quantitative Survey. IEEE Trans. Inf. Forensics Secur. 18 (2023)
Michael Thomas Smith, Kathrin Grosse, Michael Backes, Mauricio A. Álvarez
Adversarial vulnerability bounds for Gaussian process classification. Mach. Learn. 112 (3) (2023)
Kathrin Grosse, Lukas Bieringer, Tarek R. Besold, Battista Biggio, Katharina Krombholz
Machine Learning Security in Industry: A Quantitative Survey. IEEE Trans. Instrum. Meas. 72 (2023)
Antonio Emanuele Cinà, Kathrin Grosse, Ambra Demontis, Sebastiano Vascon, Werner Zellinger, Bernhard Alois Moser, Alina Oprea, Battista Biggio, Marcello Pelillo, Fabio Roli
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning. ACM Comput. Surv. 55 (13s) (2023)
Kathrin Grosse, Lukas Bieringer, Tarek Richard Besold, Alexandre Alahi
Towards more Practical Threat Models in Artificial Intelligence Security. CoRR (2023)
Antonio Emanuele Cinà, Kathrin Grosse, Ambra Demontis, Sebastiano Vascon, Werner Zellinger, Bernhard Alois Moser, Alina Oprea, Battista Biggio, Marcello Pelillo, Fabio Roli
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning. CoRR (2022)
Kathrin Grosse, Lukas Bieringer, Tarek Richard Besold, Battista Biggio, Katharina Krombholz
"Why do so?" - A Practical Perspective on Machine Learning Security. CoRR (2022)
Antonio Emanuele Cinà, Kathrin Grosse, Ambra Demontis, Battista Biggio, Fabio Roli, Marcello Pelillo
Machine Learning Security against Data Poisoning: Are We There Yet? CoRR (2022)
Ambra Demontis, Maura Pintor, Luca Demetrio, Kathrin Grosse, Hsiao-Ying Lin, Chengfang Fang, Battista Biggio, Fabio Roli
A Survey on Reinforcement Learning Security with Application to Autonomous Driving. CoRR (2022)
Lukas Bieringer, Kathrin Grosse, Michael Backes, Battista Biggio, Katharina Krombholz
Industrial practitioners' mental models of adversarial machine learning. SOUPS @ USENIX Security Symposium (2022)
Kathrin Grosse, Taesung Lee, Battista Biggio, Youngja Park, Michael Backes, Ian M. Molloy
Backdoor smoothing: Demystifying backdoor attacks on deep neural networks. Comput. Secur. 120 (2022)
Antonio Emanuele Cinà, Kathrin Grosse, Sebastiano Vascon, Ambra Demontis, Battista Biggio, Fabio Roli, Marcello Pelillo
Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions. CoRR (2021)
Lucjan Hanzlik, Yang Zhang, Kathrin Grosse, Ahmed Salem, Maximilian Augustin, Michael Backes, Mario Fritz
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service. CVPR Workshops (2021)
Kathrin Grosse, Michael Backes
Do winning tickets exist before DNN training? SDM (2021)
Lukas Bieringer, Kathrin Grosse, Michael Backes, Katharina Krombholz
Mental Models of Adversarial Machine Learning. CoRR (2021)
Kathrin Grosse, Michael T. Smith, Michael Backes
Killing Four Birds with one Gaussian Process: The Relation between different Test-Time Attacks. ICPR (2020)
Kathrin Grosse, Thomas Alexander Trost, Marius Mosbach, Michael Backes, Dietrich Klakow
On the Security Relevance of Initial Weights in Deep Neural Networks. ICANN (1) (2020)
Kathrin Grosse, Taesung Lee, Youngja Park, Michael Backes, Ian M. Molloy
A new measure for overfitting and its implications for backdooring of deep learning. CoRR (2020)
Nico Döttling, Kathrin Grosse, Michael Backes, Ian M. Molloy
Adversarial Examples and Metrics. CoRR (2020)
Kathrin Grosse, Michael Backes
How many winning tickets are there in one DNN? CoRR (2020)
Michael Thomas Smith, Kathrin Grosse, Michael Backes, Mauricio A. Álvarez
Adversarial Vulnerability Bounds for Gaussian Process Classification. CoRR (2019)
Kathrin Grosse, Thomas Alexander Trost, Marius Mosbach, Michael Backes, Dietrich Klakow
Adversarial Initialization - when your network performs the way I want. CoRR (2019)
Kathrin Grosse, Michael T. Smith, Michael Backes
Killing Three Birds with one Gaussian Process: Analyzing Attack Vectors on Classification. CoRR (2018)
Kathrin Grosse, David Pfaff, Michael T. Smith, Michael Backes
The Limitations of Model Uncertainty in Adversarial Settings. CoRR (2018)
Lucjan Hanzlik, Yang Zhang, Kathrin Grosse, Ahmed Salem, Max Augustin, Michael Backes, Mario Fritz
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service. CoRR (2018)
Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, Patrick D. McDaniel
On the (Statistical) Detection of Adversarial Examples. CoRR (2017)
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick D. McDaniel
Adversarial Examples for Malware Detection. ESORICS (2) (2017)
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick D. McDaniel
Adversarial Perturbations Against Deep Neural Networks for Malware Classification. CoRR (2016)
Kathrin Grosse, María Paula González, Carlos Iván Chesñevar, Ana Gabriela Maguitman
Integrating argumentation and sentiment analysis for mining opinions from Twitter. AI Commun. 28 (3) (2015)
Carlos Iván Chesñevar, María Paula González, Kathrin Grosse, Ana Gabriela Maguitman
A First Approach to Mining Opinions as Multisets through Argumentation. AT (2013)
Kathrin Grosse, Carlos Iván Chesñevar, Ana Gabriela Maguitman, Elsa Estevez
Empowering an E-Government Platform Through Twitter-Based Arguments. Inteligencia Artif. 15 (50) (2012)
Kathrin Grosse, Carlos Iván Chesñevar, Ana Gabriela Maguitman
An Argument-based Approach to Mining Opinions from Twitter. AT (2012)