| Sc-glowtts: an efficient zero-shot multi-speaker text-to-speech model E Casanova, C Shulby, E Gölge, NM Müller, FS de Oliveira, AC Junior, ... InterSpeech 2021, 2021 | 70 | 2021 |
| Does Audio Deepfake Detection Generalize? NM Müller, P Czempin, F Dieckmann, A Froghyar, K Böttinger Interspeech 2022, 2022 | 61 | 2022 |
| Identifying Mislabeled Instances in Classification Datasets NM Müller, K Markert 2019 International Joint Conference on Neural Networks (IJCNN), 2019 | 48 | 2019 |
| Speech is silver, silence is golden: What do asvspoof-trained models really learn? NM Müller, F Dieckmann, P Czempin, R Canals, K Böttinger, J Williams ASVspoof 2021, 2021 | 46 | 2021 |
| Human perception of audio deepfakes NM Müller, K Markert, J Williams First International Workshop on Deepfake Detection for Audio Multimedia at …, 2021 | 41 | 2021 |
| Data poisoning attacks on regression learning and corresponding defenses N Müller, D Kowatsch, K Böttinger 2020 IEEE 25th Pacific Rim International Symposium on Dependable Computing …, 2020 | 19 | 2020 |
| On GDPR compliance of companies’ privacy policies NM Müller, D Kowatsch, P Debus, D Mirdita, K Böttinger Text, Speech, and Dialogue: 22nd International Conference, TSD 2019 …, 2019 | 17 | 2019 |
| Distributed Anomaly Detection of Single Mote Attacks in RPL Networks N Müller, P Debus, DKK Böttinger Proceedings of the 16th International Joint Conference on e-Business and …, 2019 | 15 | 2019 |
| Attacker Attribution of Audio Deepfakes NM Müller, F Dieckmann, J Williams Interspeech 2022, 2022 | 9 | 2022 |
| A. d. S. Soares, SM Aluisio, and MA Ponti,“Sc-glowtts: an efficient zero-shot multi-speaker text-to-speech model,” E Casanova, C Shulby, E Gölge, NM Müller, FS de Oliveira, AC Junior arXiv preprint arXiv:2104.05557, 2021 | 7 | 2021 |
| Towards resistant audio adversarial examples T Dörr, K Markert, NM Müller, K Böttinger Proceedings of the 1st ACM Workshop on Security and Privacy on Artificial …, 2020 | 7 | 2020 |
| Complex-valued neural networks for voice anti-spoofing NM Müller, P Sperl, K Böttinger arXiv preprint arXiv:2308.11800, 2023 | 3 | 2023 |
| MLAAD: The Multi-Language Audio Anti-Spoofing Dataset NM Müller, P Kawa, WH Choong, E Casanova, E Gölge, T Müller, P Syga, ... arXiv preprint arXiv:2401.09512, 2024 | 1 | 2024 |
| Shortcut detection with variational autoencoders NM Müller, S Roschmann, S Khan, P Sperl, K Böttinger arXiv preprint arXiv:2302.04246, 2023 | 1 | 2023 |
| Deep Reinforcement Learning for Backup Strategies against Adversaries P Debus, N Müller, K Böttinger arXiv preprint arXiv:2102.06632, 2021 | 1 | 2021 |
| Adversarial vulnerability of active transfer learning NM Müller, K Böttinger Advances in Intelligent Data Analysis XIX: 19th International Symposium on …, 2021 | 1 | 2021 |
| Defending against adversarial denial-of-service data poisoning attacks NM Müller, S Roschmann, K Böttinger Proceedings of the 2020 Workshop on DYnamic and Novel Advances in Machine …, 2020 | 1 | 2020 |
| Imbalance in Regression Datasets D Kowatsch, NM Müller, K Tscharke, P Sperl, K Bötinger arXiv preprint arXiv:2402.11963, 2024 | | 2024 |
| A New Approach to Voice Authenticity NM Müller, P Kawa, S Hu, M Neu, J Williams, P Sperl, K Böttinger arXiv preprint arXiv:2402.06304, 2024 | | 2024 |
| Protecting Publicly Available Data With Machine Learning Shortcuts NM Müller, M Burgert, P Debus, J Williams, P Sperl, K Böttinger arXiv preprint arXiv:2310.19381, 2023 | | 2023 |
Konstantin BöttingerFraunhofer AISECVerified email at aisec.fraunhofer.de
