| Detecting adversarial examples-a lesson from multimedia security P Schöttle, A Schlögl, C Pasquini, R Böhme 2018 26th European Signal Processing Conference (EUSIPCO), 947-951, 2018 | 31 | 2018 |
| eNNclave: Offline inference with model confidentiality A Schlögl, R Böhme Proceedings of the 13th ACM Workshop on Artificial Intelligence and Security …, 2020 | 27 | 2020 |
| Causes and effects of unanticipated numerical deviations in neural network inference frameworks A Schlögl, N Hofer, R Böhme Advances in Neural Information Processing Systems 36, 2024 | 8 | 2024 |
| Detecting adversarial examples-a lesson from multimedia forensics P Schöttle, A Schlögl, C Pasquini, R Böhme arXiv preprint arXiv:1803.03613, 2018 | 7 | 2018 |
| Forensicability of deep neural network inference pipelines A Schlögl, T Kupek, R Böhme ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 5 | 2021 |
| iNNformant: Boundary samples as telltale watermarks A Schlögl, T Kupek, R Böhme Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia …, 2021 | 2 | 2021 |
| Defending against power analysis by balancing binary values A Schlögl | | |