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 | 1098* | 2017 |
On the (statistical) detection of adversarial examples K Grosse, P Manoharan, N Papernot, M Backes, P McDaniel arXiv preprint arXiv:1702.06280, 2017 | 863 | 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 | 114 | 2021 |
Wild patterns reloaded: A survey of machine learning security against training data poisoning AE Cinà, K Grosse, A Demontis, S Vascon, W Zellinger, BA Moser, ... ACM Computing Surveys 55 (13s), 1-39, 2023 | 80 | 2023 |
The limitations of model uncertainty in adversarial settings K Grosse, D Pfaff, MT Smith, M Backes arXiv preprint arXiv:1812.02606, 2018 | 51* | 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 | 49 | 2015 |
Machine learning security against data poisoning: Are we there yet? AE Cinà, K Grosse, A Demontis, B Biggio, F Roli, M Pelillo Computer 57 (3), 26-34, 2024 | 33 | 2024 |
An Argument-based Approach to Mining Opinions from Twitter. K Grosse, CI Chesñevar, AG Maguitman AT 918, 408-422, 2012 | 32 | 2012 |
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 | 26* | 2022 |
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 | 25* | 2023 |
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 | 15* | 2022 |
Backdoor learning curves: Explaining backdoor poisoning beyond influence functions AE Cinà, K Grosse, S Vascon, A Demontis, B Biggio, F Roli, M Pelillo arXiv preprint arXiv:2106.07214, 2021 | 15 | 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 | 12* | 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 | 11* | 2021 |
Adversarial vulnerability bounds for Gaussian process classification MT Smith, K Grosse, M Backes, MA Alvarez Machine Learning 112 (3), 971-1009, 2023 | 9 | 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 | 8 | 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 | 6 | 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 | 4* | 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 | 3 | 2022 |
Do winning tickets exist before DNN training? K Grosse, M Backes Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021 | 3* | 2021 |