Kathrin Grosse
Kathrin Grosse
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Cited by
Cited by
Adversarial examples for malware detection
K Grosse, N Papernot, P Manoharan, M Backes, P McDaniel
Computer Security–ESORICS 2017: 22nd European Symposium on Research in …, 2017
On the (statistical) detection of adversarial examples
K Grosse, P Manoharan, N Papernot, M Backes, P McDaniel
arXiv preprint arXiv:1702.06280, 2017
Mlcapsule: Guarded offline deployment of machine learning as a service
L Hanzlik, Y Zhang, K Grosse, A Salem, M Augustin, M Backes, M Fritz
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021
Wild patterns reloaded: A survey of machine learning security against training data poisoning
AE Cinā, K Grosse, A Demontis, S Vascon, W Zellinger, BA Moser, ...
ACM Computing Surveys 55 (13s), 1-39, 2023
The limitations of model uncertainty in adversarial settings
K Grosse, D Pfaff, MT Smith, M Backes
arXiv preprint arXiv:1812.02606, 2018
Integrating argumentation and sentiment analysis for mining opinions from Twitter
K Grosse, MP Gonzalez, CI Chesnevar, AG Maguitman
AI Communications 28 (3), 387-401, 2015
Machine learning security against data poisoning: Are we there yet?
AE Cinā, K Grosse, A Demontis, B Biggio, F Roli, M Pelillo
Computer 57 (3), 26-34, 2024
An Argument-based Approach to Mining Opinions from Twitter.
K Grosse, CI Chesņevar, AG Maguitman
AT 918, 408-422, 2012
Machine learning security in industry: A quantitative survey
K Grosse, L Bieringer, TR Besold, B Biggio, K Krombholz
IEEE Transactions on Information Forensics and Security 18, 1749-1762, 2023
Industrial practitioners' mental models of adversarial machine learning
L Bieringer, K Grosse, M Backes, B Biggio, K Krombholz
Eighteenth Symposium on Usable Privacy and Security (SOUPS 2022), 97-116, 2022
Backdoor smoothing: Demystifying backdoor attacks on deep neural networks
K Grosse, T Lee, B Biggio, Y Park, M Backes, I Molloy
Computers & Security 120, 102814, 2022
Backdoor learning curves: Explaining backdoor poisoning beyond influence functions
AE Cinā, K Grosse, S Vascon, A Demontis, B Biggio, F Roli, M Pelillo
arXiv preprint arXiv:2106.07214, 2021
On the security relevance of initial weights in deep neural networks
K Grosse, TA Trost, M Mosbach, M Backes, D Klakow
Artificial Neural Networks and Machine Learning–ICANN 2020: 29th …, 2020
Killing four birds with one Gaussian process: The relation between different test-time attacks
K Grosse, MT Smith, M Backes
2020 25th International Conference on Pattern Recognition (ICPR), 4696-4703, 2021
Adversarial vulnerability bounds for Gaussian process classification
MT Smith, K Grosse, M Backes, MA Alvarez
Machine Learning 112 (3), 971-1009, 2023
Empowering an e-government platform through twitter-based arguments
K Grosse, C Chesnevar, A Maguitman, E Estevez
Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial …, 2012
Measuring overfitting of machine learning computer model and susceptibility to security threats
K Grosse, T Lee, Y Park, IM Molloy
US Patent 11,494,496, 2022
Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness
D Fernández Llorca, R Hamon, H Junklewitz, K Grosse, L Kunze, ...
arXiv e-prints, arXiv: 2403.14641, 2024
A survey on reinforcement learning security with application to autonomous driving
A Demontis, M Pintor, L Demetrio, K Grosse, HY Lin, C Fang, B Biggio, ...
arXiv preprint arXiv:2212.06123, 2022
Do winning tickets exist before DNN training?
K Grosse, M Backes
Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021
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