| Does Audio Deepfake Detection Generalize? NM Müller, P Czempin, F Dieckmann, A Froghyar, K Böttinger Interspeech 2022, 2022 | 103 | 2022 |
| 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 | 79 | 2021 |
| 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 | 62 | 2021 |
| Identifying Mislabeled Instances in Classification Datasets NM Müller, K Markert 2019 International Joint Conference on Neural Networks (IJCNN), 2019 | 58 | 2019 |
| Human perception of audio deepfakes NM Müller, K Markert, J Williams First International Workshop on Deepfake Detection for Audio Multimedia at …, 2021 | 57 | 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 | 24 | 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 | 18 | 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 | 17 | 2019 |
| 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 | 12 | 2024 |
| Attacker Attribution of Audio Deepfakes NM Müller, F Dieckmann, J Williams Interspeech 2022, 2022 | 12 | 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 | 11 | 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 | 8 | 2020 |
| Complex-valued neural networks for voice anti-spoofing NM Müller, P Sperl, K Böttinger arXiv preprint arXiv:2308.11800, 2023 | 7 | 2023 |
| Harder or Different? Understanding Generalization of Audio Deepfake Detection NM Müller, N Evans, H Tak, P Sperl, K Böttinger arXiv preprint arXiv:2406.03512, 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 |
| Localized Shortcut Removal NM Müller, J Jacobs, J Williams, K Böttinger Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 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 |
| Easy, Interpretable, Effective: openSMILE for voice deepfake detection O Pascu, D Oneata, H Cucu, NM Müller arXiv preprint arXiv:2408.15775, 2024 | | 2024 |
Konstantin BöttingerFraunhofer AISECVerified email at aisec.fraunhofer.de