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2020 – today
- 2024
[c12]Nicolas M. Müller, Piotr Kawa, Wei Herng Choong, Edresson Casanova, Eren Gölge, Thorsten Müller, Piotr Syga, Philip Sperl, Konstantin Böttinger:
MLAAD: The Multi-Language Audio Anti-Spoofing Dataset. IJCNN 2024: 1-7- [c11]Nicolas M. Müller, Simon Roschmann, Shahbaz Khan, Philip Sperl, Konstantin Böttinger:
Shortcut Detection With Variational Autoencoders. IJCNN 2024: 1-7 - [i17]Nicolas M. Müller, Piotr Kawa, Wei Herng Choong, Edresson Casanova, Eren Gölge, Thorsten Müller, Piotr Syga, Philip Sperl, Konstantin Böttinger:
MLAAD: The Multi-Language Audio Anti-Spoofing Dataset. CoRR abs/2401.09512 (2024) - [i16]Nicolas M. Müller, Piotr Kawa, Shen Hu, Matthias Neu, Jennifer Williams, Philip Sperl, Konstantin Böttinger:
A New Approach to Voice Authenticity. CoRR abs/2402.06304 (2024) - [i15]Daniel Kowatsch, Nicolas M. Müller, Kilian Tscharke, Philip Sperl, Konstantin Böttinger:
Imbalance in Regression Datasets. CoRR abs/2402.11963 (2024) - [i14]Nicolas M. Müller, Nicholas W. D. Evans, Hemlata Tak, Philip Sperl, Konstantin Böttinger:
Harder or Different? Understanding Generalization of Audio Deepfake Detection. CoRR abs/2406.03512 (2024) - 2023
- [b1]Philip Sperl:
Defending Neural Networks with Activation Analysis. Technical University of Munich, Germany, 2023 - [c10]Nicolas M. Müller, Maximilian Burgert, Pascal Debus, Jennifer Williams, Philip Sperl, Konstantin Böttinger:
Protecting Publicly Available Data With Machine Learning Shortcuts. BMVC 2023: 685-698 - [c9]Nicolas M. Müller, Philip Sperl, Konstantin Böttinger:
Complex-valued neural networks for voice anti-spoofing. INTERSPEECH 2023: 3814-3818 - [i13]Nicolas M. Müller, Simon Roschmann, Shahbaz Khan, Philip Sperl, Konstantin Böttinger:
Shortcut Detection with Variational Autoencoders. CoRR abs/2302.04246 (2023) - [i12]Nicolas M. Müller, Philip Sperl, Konstantin Böttinger:
Complex-valued neural networks for voice anti-spoofing. CoRR abs/2308.11800 (2023) - [i11]Nicolas M. Müller, Maximilian Burgert, Pascal Debus
, Jennifer Williams, Philip Sperl, Konstantin Böttinger:
Protecting Publicly Available Data With Machine Learning Shortcuts. CoRR abs/2310.19381 (2023) - [i10]Ana Radutoiu, Jan-Philipp Schulze, Philip Sperl, Konstantin Böttinger:
Physical Adversarial Examples for Multi-Camera Systems. CoRR abs/2311.08539 (2023) - 2022
- [c8]Paul Andrei Sava, Jan-Philipp Schulze, Philip Sperl, Konstantin Böttinger:
Assessing the Impact of Transformations on Physical Adversarial Attacks. AISec@CCS 2022: 79-90 - [c7]Jan-Philipp Schulze, Philip Sperl, Konstantin Böttinger:
Anomaly Detection by Recombining Gated Unsupervised Experts. IJCNN 2022: 1-8 - [c6]Jan-Philipp Schulze, Philip Sperl, Konstantin Böttinger:
Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators. IJCNN 2022: 1-8 - [c5]Jan-Philipp Schulze, Philip Sperl, Ana Radutoiu, Carla Sagebiel, Konstantin Böttinger:
R2-AD2: Detecting Anomalies by Analysing the Raw Gradient. ECML/PKDD (1) 2022: 209-224 - [i9]Karla Markert, Romain Parracone, Mykhailo Kulakov, Philip Sperl, Ching-Yu Kao, Konstantin Böttinger:
Visualizing Automatic Speech Recognition - Means for a Better Understanding? CoRR abs/2202.00673 (2022) - [i8]Jan-Philipp Schulze, Philip Sperl, Ana Radutoiu, Carla Sagebiel, Konstantin Böttinger:
R2-AD2: Detecting Anomalies by Analysing the Raw Gradient. CoRR abs/2206.10259 (2022) - 2021
- [c4]Jan-Philipp Schulze, Philip Sperl, Konstantin Böttinger:
DA3G: Detecting Adversarial Attacks by Analysing Gradients. ESORICS (1) 2021: 563-583 - [i7]Jan-Philipp Schulze, Philip Sperl, Konstantin Böttinger:
Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators. CoRR abs/2101.04645 (2021) - [i6]Franziska Boenisch, Philip Sperl, Konstantin Böttinger:
Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning. CoRR abs/2105.07985 (2021) - 2020
- [c3]Philip Sperl, Ching-Yu Kao, Peng Chen, Xiao Lei, Konstantin Böttinger:
DLA: Dense-Layer-Analysis for Adversarial Example Detection. EuroS&P 2020: 198-215 - [c2]Philip Sperl, Jan-Philipp Schulze, Konstantin Böttinger:
Activation Anomaly Analysis. ECML/PKDD (2) 2020: 69-84 - [i5]Philip Sperl, Jan-Philipp Schulze, Konstantin Böttinger:
A3: Activation Anomaly Analysis. CoRR abs/2003.01801 (2020) - [i4]Philip Sperl, Konstantin Böttinger:
Optimizing Information Loss Towards Robust Neural Networks. CoRR abs/2008.03072 (2020) - [i3]Jan-Philipp Schulze, Philip Sperl, Konstantin Böttinger:
Anomaly Detection by Recombining Gated Unsupervised Experts. CoRR abs/2008.13763 (2020)
2010 – 2019
- 2019
- [c1]Philip Sperl, Konstantin Böttinger:
Side-Channel Aware Fuzzing. ESORICS (1) 2019: 259-278 - [i2]Philip Sperl, Konstantin Böttinger:
Side-Channel Aware Fuzzing. CoRR abs/1908.05012 (2019) - [i1]Philip Sperl, Ching-yu Kao, Peng Chen, Konstantin Böttinger:
DLA: Dense-Layer-Analysis for Adversarial Example Detection. CoRR abs/1911.01921 (2019)
Coauthor Index
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last updated on 2024-10-07 21:23 CEST by the dblp team
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