Daniel Tenbrinck

42 papers A 2B 1Journal 24Unranked 14

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Duality Perspective on Nonlinear Eigenproblems. CoRR Jonathan Laubmann, Manuel Friedrich, Daniel Tenbrinck
2025	J	`jnl`	On MAP estimates and source conditions for drift identification in SDEs. CoRR Daniel Tenbrinck, Nikolas Uesseler, Philipp Wacker, Benedikt Wirth
2025	—	`conf`	VoxATtack: A Multimodal Attack on Voice Anonymization Systems. WASPAA Ahmad Aloradi, Ünal Ege Gaznepoglu, Emanuël A. P. Habets, Daniel Tenbrinck
2025	A	`conf`	You Are What You Say: Exploiting Linguistic Content for VoicePrivacy Attacks. INTERSPEECH Ünal Ege Gaznepoglu, Anna Leschanowsky, Ahmad Aloradi, Prachi Singh, Daniel Tenbrinck, Emanuël A. P. Habets, Nils Peters
2025	J	`jnl`	You Are What You Say: Exploiting Linguistic Content for VoicePrivacy Attacks. CoRR Ünal Ege Gaznepoglu, Anna Leschanowsky, Ahmad Aloradi, Prachi Singh, Daniel Tenbrinck, Emanuël A. P. Habets, Nils Peters
2024	J	`jnl`	Hypergraph p-Laplacians and Scale Spaces. J. Math. Imaging Vis. Ariane Fazeny, Daniel Tenbrinck, Kseniia Lukin, Martin Burger
2024	J	`jnl`	The Infinity Laplacian Eigenvalue Problem: Reformulation and a Numerical Scheme. J. Sci. Comput. Farid Bozorgnia, Leon Bungert, Daniel Tenbrinck
2023	—	`conf`	Hypergraph p-Laplacians, Scale Spaces, and Information Flow in Networks. SSVM Ariane Fazeny, Daniel Tenbrinck, Martin Burger
2023	J	`jnl`	Hypergraph p-Laplacians, Scale Spaces, and Information Flow in Networks. CoRR Ariane Fazeny, Daniel Tenbrinck, Martin Burger
2023	—	`conf`	Resolution-Invariant Image Classification Based on Fourier Neural Operators. SSVM Samira Kabri, Tim Roith, Daniel Tenbrinck, Martin Burger
2023	J	`jnl`	Resolution-Invariant Image Classification based on Fourier Neural Operators. CoRR Samira Kabri, Tim Roith, Daniel Tenbrinck, Martin Burger
2022	J	`jnl`	A Bregman Learning Framework for Sparse Neural Networks. J. Mach. Learn. Res. Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger
2021	J	`jnl`	A Bregman Learning Framework for Sparse Neural Networks. CoRR Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger
2021	—	`conf`	CLIP: Cheap Lipschitz Training of Neural Networks. SSVM Leon Bungert, René Raab, Tim Roith, Leo Schwinn, Daniel Tenbrinck
2021	J	`jnl`	CLIP: Cheap Lipschitz Training of Neural Networks. CoRR Leon Bungert, René Raab, Tim Roith, Leo Schwinn, Daniel Tenbrinck
2021	B	`conf`	Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks. IJCNN Leo Schwinn, An Nguyen, René Raab, Dario Zanca, Bjoern M. Eskofier, Daniel Tenbrinck, Martin Burger
2021	J	`jnl`	Fenchel Duality Theory and a Primal-Dual Algorithm on Riemannian Manifolds. Found. Comput. Math. Ronny Bergmann, Roland Herzog, Maurício Silva Louzeiro, Daniel Tenbrinck, José Vidal-Núñez
2021	J	`jnl`	Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis. CoRR Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Bjoern M. Eskofier
2021	A	`conf`	Identifying untrustworthy predictions in neural networks by geometric gradient analysis. UAI Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Björn M. Eskofier
2021	J	`jnl`	Neural Architecture Search via Bregman Iterations. CoRR Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger
2020	J	`jnl`	Sampled Nonlocal Gradients for Stronger Adversarial Attacks. CoRR Leo Schwinn, Daniel Tenbrinck, An Nguyen, René Raab, Martin Burger, Bjoern M. Eskofier
2020	J	`jnl`	The Infinity Laplacian eigenvalue problem: reformulation and a numerical scheme. CoRR Farid Bozorgnia, Leon Bungert, Daniel Tenbrinck
2019	—	`conf`	Computing Nonlinear Eigenfunctions via Gradient Flow Extinction. SSVM Leon Bungert, Martin Burger, Daniel Tenbrinck
2019	J	`jnl`	Computing Nonlinear Eigenfunctions via Gradient Flow Extinction. CoRR Leon Bungert, Martin Burger, Daniel Tenbrinck
2019	J	`jnl`	Fenchel Duality for Convex Optimization and a Primal Dual Algorithm on Riemannian Manifolds. CoRR Ronny Bergmann, Roland Herzog, Daniel Tenbrinck, José Vidal-Núñez
2019	J	`jnl`	Variational Graph Methods for Efficient Point Cloud Sparsification. CoRR Daniel Tenbrinck, Fjedor Gaede, Martin Burger
2018	J	`jnl`	A Graph Framework for Manifold-Valued Data. SIAM J. Imaging Sci. Ronny Bergmann, Daniel Tenbrinck
2017	—	`conf`	Nonlocal Inpainting of Manifold-Valued Data on Finite Weighted Graphs. GSI Ronny Bergmann, Daniel Tenbrinck
2015	J	`jnl`	Image segmentation with arbitrary noise models by solving minimal surface problems. Pattern Recognit. Daniel Tenbrinck, Xiaoyi Jiang
2015	J	`jnl`	On the p-Laplacian and ∞-Laplacian on Graphs with Applications in Image and Data Processing. SIAM J. Imaging Sci. Abderrahim Elmoataz, Matthieu Toutain, Daniel Tenbrinck
2015	—	`conf`	Solving Minimal Surface Problems on Surfaces and Point Clouds. SSVM Daniel Tenbrinck, François Lozes, Abderrahim Elmoataz
2014	J	`jnl`	Automatic classification of left ventricular wall segments in small animal ultrasound imaging. Comput. Methods Programs Biomed. Kathrin Ungru, Daniel Tenbrinck, Xiaoyi Jiang, Jörg Stypmann
2014	—	`conf`	Registration of Noisy Images via Maximum A-Posteriori Estimation. WBIR Sebastian Suhr, Daniel Tenbrinck, Martin Burger, Jan Modersitzki
2013	J	`jnl`	A Variational Framework for Region-Based Segmentation Incorporating Physical Noise Models. J. Math. Imaging Vis. Alex Sawatzky, Daniel Tenbrinck, Xiaoyi Jiang, Martin Burger
2013	—	`conf`	Biomedical Imaging: A Computer Vision Perspective. CAIP (1) Xiaoyi Jiang, Mohammad Dawood, Fabian Gigengack, Benjamin Risse, Sönke Schmid, Daniel Tenbrinck, Klaus P. Schäfers
2013	—	`conf`	Discriminant Analysis Based Level Set Segmentation for Ultrasound Imaging. CAIP (2) Daniel Tenbrinck, Xiaoyi Jiang
2013	J	`jnl`	Histogram-Based Optical Flow for Motion Estimation in Ultrasound Imaging. J. Math. Imaging Vis. Daniel Tenbrinck, Sönke Schmid, Xiaoyi Jiang, Klaus P. Schäfers, Jörg Stypmann
2013	—	`conf`	Region Based Contour Detection by Dynamic Programming. CAIP (2) Xiaoyi Jiang, Daniel Tenbrinck
2013	—	`—`	Variational Methods for Medical Ultrasound Imaging. Daniel Tenbrinck
2012	—	`conf`	Impact of Physical Noise Modeling on Image Segmentation in Echocardiography. VCBM Daniel Tenbrinck, Alex Sawatzky, Xiaoyi Jiang, Martin Burger, Wladimir Haffner, Patrick Willems, Matthias Paul, Jörg Stypmann
2011	—	`conf`	Histogram-Based Optical Flow for Functional Imaging in Echocardiography. CAIP (1) Sönke Schmid, Daniel Tenbrinck, Xiaoyi Jiang, Klaus P. Schäfers, Klaus Tiemann, Jörg Stypmann
2010	—	`conf`	Motion correction in positron emission tomography considering partial volume effects in optical flow estimation. ISBI Daniel Tenbrinck, Mohammad Dawood, Fabian Gigengack, Michael Fieseler, Xiaoyi Jiang, Klaus Schiifers

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.