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 | 35 | 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 | 29 | 2022 |
Towards more Practical Threat Models in Artificial Intelligence Security K Grosse, L Bieringer, TR Besold, AM Alahi 33rd USENIX Security Symposium (USENIX Security 24), 4891-4908, 2024 | 9 | 2024 |
Mental models of adversarial machine learning L Bieringer, K Grosse, M Backes, B Biggio, K Krombholz arXiv preprint arXiv:2105.03726, 2021 | 8 | 2021 |
Why do so?”-A Practical Perspective on Machine Learning Security K Grosse, L Bieringer, TR Besold, B Biggio, K Krombholz Int. Conf. Machin. Learn.: New Frontiers of Adversarial Machine Learning, 2022 | 7 | 2022 |
When Your AI Becomes a Target: AI Security Incidents and Best Practices K Grosse, L Bieringer, TR Besold, B Biggio, A Alahi Proceedings of the AAAI Conference on Artificial Intelligence 38 (21), 23041 …, 2024 | 4 | 2024 |
Position: A taxonomy for reporting and describing AI security incidents L Bieringer, K Paeth, A Wespi, K Grosse arXiv preprint arXiv:2412.14855, 2024 | | 2024 |