Side-Channel Aware Fuzzing. - Philip Sperl; Konstantin Böttinger (ESORICS (1), 2019)

DLA: Dense-Layer-Analysis for Adversarial Example Detection. - Philip Sperl; Ching-yu Kao; Peng Chen; Konstantin Böttinger (CoRR, 2019)

DLA: Dense-Layer-Analysis for Adversarial Example Detection. - Philip Sperl; Ching-Yu Kao; Peng Chen; Xiao Lei; Konstantin Böttinger (EuroS&P, 2020)

Activation Anomaly Analysis. - Philip Sperl; Jan-Philipp Schulze; Konstantin Böttinger (ECML/PKDD (2), 2020)

A3: Activation Anomaly Analysis. - Philip Sperl; Jan-Philipp Schulze; Konstantin Böttinger (CoRR, 2020)

Optimizing Information Loss Towards Robust Neural Networks. - Philip Sperl; Konstantin Böttinger (CoRR, 2020)

DA3G: Detecting Adversarial Attacks by Analysing Gradients. - Jan-Philipp Schulze; Philip Sperl; Konstantin Böttinger (ESORICS (1), 2021)

Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning. - Franziska Boenisch; Philip Sperl; Konstantin Böttinger (CoRR, 2021)

Assessing the Impact of Transformations on Physical Adversarial Attacks. - Paul Andrei Sava; Jan-Philipp Schulze; Philip Sperl; Konstantin Böttinger (AISec@CCS, 2022)

Anomaly Detection by Recombining Gated Unsupervised Experts. - Jan-Philipp Schulze; Philip Sperl; Konstantin Böttinger (IJCNN, 2022)

Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators. - Jan-Philipp Schulze; Philip Sperl; Konstantin Böttinger (IJCNN, 2022)

R2-AD2: Detecting Anomalies by Analysing the Raw Gradient. - Jan-Philipp Schulze; Philip Sperl; Ana Radutoiu; Carla Sagebiel; Konstantin Böttinger (ECML/PKDD (1), 2022)

Visualizing Automatic Speech Recognition - Means for a Better Understanding? - Karla Markert; Romain Parracone; Mykhailo Kulakov; Philip Sperl; Ching-Yu Kao; Konstantin Böttinger (CoRR, 2022)

Protecting Publicly Available Data With Machine Learning Shortcuts. - Nicolas M. Müller; Maximilian Burgert; Pascal Debus; Jennifer Williams 001; Philip Sperl; Konstantin Böttinger (BMVC, 2023)

Complex-valued neural networks for voice anti-spoofing. - Nicolas M. Müller; Philip Sperl; Konstantin Böttinger (INTERSPEECH, 2023)

Physical Adversarial Examples for Multi-Camera Systems. - Ana Radutoiu; Jan-Philipp Schulze; Philip Sperl; Konstantin Böttinger (CoRR, 2023)

Defending Neural Networks with Activation Analysis. - Philip Sperl (2023)

MLAAD: The Multi-Language Audio Anti-Spoofing Dataset. - Nicolas M. Müller; Piotr Kawa; Wei Herng Choong; Edresson Casanova; Eren Gölge; Thorsten Müller; Piotr Syga; Philip Sperl; Konstantin Böttinger (IJCNN, 2024)

Shortcut Detection With Variational Autoencoders. - Nicolas M. Müller; Simon Roschmann; Shahbaz Khan 009; Philip Sperl; Konstantin Böttinger (IJCNN, 2024)

Harder or Different? Understanding Generalization of Audio Deepfake Detection. - Nicolas M. Müller; Nicholas W. D. Evans; Hemlata Tak; Philip Sperl; Konstantin Böttinger (INTERSPEECH, 2024)

A New Approach to Voice Authenticity. - Nicolas M. Müller; Piotr Kawa; Shen Hu; Matthias Neu; Jennifer Williams 001; Philip Sperl; Konstantin Böttinger (INTERSPEECH, 2024)

Imbalance in Regression Datasets. - Daniel Kowatsch; Nicolas M. Müller; Kilian Tscharke; Philip Sperl; Konstantin Böttinger (CoRR, 2024)

DeePen: Penetration Testing for Audio Deepfake Detection. - Nicolas M. Müller; Piotr Kawa; Adriana Stan; Thien-Phuc Doan; Souhwan Jung; Wei Herng Choong; Philip Sperl; Konstantin Böttinger (CoRR, 2025)

Replay Attacks Against Audio Deepfake Detection. - Nicolas M. Müller; Piotr Kawa; Wei Herng Choong; Adriana Stan; Aditya Tirumala Bukkapatnam; Karla Pizzi; Alexander Wagner; Philip Sperl (CoRR, 2025)