| Activation Anomaly Analysis P Sperl, JP Schulze, K Böttinger Machine Learning and Knowledge Discovery in Databases: European Conference …, 2021 | 16 | 2021 |
| Assessing the Impact of Transformations on Physical Adversarial Attacks PA Sava, JP Schulze, P Sperl, K Böttinger Proceedings of the 15th ACM Workshop on Artificial Intelligence and Security …, 2022 | 7 | 2022 |
| Double-adversarial activation anomaly detection: Adversarial autoencoders are anomaly generators JP Schulze, P Sperl, K Böttinger 2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022 | 7 | 2022 |
| DA3G: Detecting Adversarial Attacks by Analysing Gradients JP Schulze, P Sperl, K Böttinger European Symposium on Research in Computer Security, 563-583, 2021 | 7 | 2021 |
| Context by Proxy: Identifying Contextual Anomalies Using an Output Proxy JP Schulze, A Mrowca, E Ren, HA Loeliger, K Böttinger Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 5 | 2019 |
| R2-ad2: Detecting anomalies by analysing the raw gradient JP Schulze, P Sperl, A Răduțoiu, C Sagebiel, K Böttinger Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 3 | 2022 |
| Anomaly Detection by Recombining Gated Unsupervised Experts JP Schulze, P Sperl, K Böttinger 2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022 | 1 | 2022 |
| Security of AI-Systems: Fundamentals L Adilova, K Böttinger, V Danos, S Jacob, F Langer, T Markert, ... | 1 | 2022 |
| Data-Driven Performance Correlation JP Schulze | | |
Konstantin BöttingerFraunhofer AISECVerified email at aisec.fraunhofer.de
