Prof. Dr. Pascal Schöttle

Head of Josef Ressel Centre for Security Analysis of IoT DevicesIT Security & Machine Learning +43 512 2070 - 4332 pascal.schoettle@mci.edu

Peer reviewed journal article

Merkle, F., Weber, D., Schöttle, P., Schlögl, S., & Nocker, M. (2025). Less is More: The Influence of Pruning on the Explainability of CNNs. IEEE Access, 13, 87909–87927. https://doi.org/10.1109/ACCESS.2025.3569575
Merkle, F., Samsinger, M., Schöttle, P., & Pevny, T. (2024). On the Economics of Adversarial Machine Learning. IEEE Transactions on Information Forensics and Security, 1–1. https://doi.org/10.1109/TIFS.2024.3379829
Brinkschulte, L., Schlögl, S., Monz, A., Schöttle, P., & Janetschek, M. (2022). Perspectives on Socially Intelligent Conversational Agents. Multimodal Technologies and Interaction, 6(8). https://doi.org/10.3390/mti6080062
Baumhauer, T., Schöttle, P., & Zeppelzauer, M. (2022). Machine unlearning: linear filtration for logit-based classifiers. Machine Learning. https://doi.org/10.1007/s10994-022-06178-9
Thalhammer, F., Schöttle, P., Janetschek, M., & Ploder, C. (2022). Blockchain Use Cases Against Climate Destruction. Cloud Computing and Data Science, 3(2), 22–38. https://doi.org/10.37256/ccds.3220221277
Pascal Schöttle and Rainer Böhme. “Game Theory and Adaptive Steganography”. In: IEEE Transactions on Information Forensics and Security 11.4 (2016), pp. 760–773
Benjamin Johnson, Pascal Schöttle, Aron Laszka, Jens Grossklags, and Rainer Böhme. “Adaptive Steganography and Steganalysis with Fixed-Size Embedding”. In: Transactions on Data Hiding and Multimedia Security 10 (2015), pp. 69–91
Aron Laszka, Benjamin Johnson, Pascal Schöttle, Jens Grossklags, and Rainer Böhme. “Secure Team Composition to Thwart Insider Threats and Cyberespionage”. In: ACM Transactions on Internet Technology 14.2–3 (2014), 19:1–22

Editorial-reviewed journal

Pascal Schöttle and Rainer Böhme. “Die totale Transparenz. Facebook, Cookies, RFID etc.” In: Ethische Herausforderungen im Web 2.0. Ed. by Martin Dabrowski, Judith Wolf, and Karlies Abmeier. Paderborn: Schöningh, 2014, pp. 11–31

Chapters in books

Janetschek, M., & Schöttle, P. (2024). Technische Grundlagen. In L. Staffler, B. Ebersberger, & A. Jobin (Eds.), Digitalwirtschaft: Technische, wirtschaftliche und gesellschaftliche Grundlagen (1st ed., p. 474). Springer Gabler Wiesbaden.

Peer reviewed academic/professional meeting proceedings

Schöttle, P., Janetschek, M., Merkle, F., Nocker, M., & Egger, C. (2025). Large-Scale (Semi-)Automated Security Assessment of Consumer IoT Devices – A Roadmap. 2025 10th International Conference on Smart and Sustainable Technologies (SpliTech), 1–8. https://doi.org/10.23919/SpliTech65624.2025.11091811
Nocker, M., Henke, L., & Schottle, P. (2025). FHE ML Tuxedo: A Tailored Wrapper Architecture for Homomorphic Encryption in Machine Learning. 2025 55th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 266–273. https://doi.org/10.1109/DSN-W65791.2025.00075
Russold, M., Nocker, M., & Schöttle, P. (2024). Incremental Whole Plate ALPR Under Data Availability Constraints. Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM, 131–140. https://doi.org/10.5220/0012566400003654
Merkle, F., Sirbu, M. R., Nocker, M., & Schöttle, P. (2024). Generating Invariance-Based Adversarial Examples: Bringing Humans Back into the Loop. In G. L. Foresti, A. Fusiello, & E. Hancock (Hrsg.), Image Analysis and Processing—ICIAP 2023 Workshops (S. 15–27). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-51023-6_2
Martin, Nocker, David, Drexel, Michael Rader, Alessio Montuoro, and Pascal Schöttle. "HE-MAN - Homomorphically Encrypted MAchine learning with oNnx models", In The 8th International Conference on Machine Learning Technologies (ICMLT), 2023. https://doi.org/10.1145/3589883.3589889
Roland Rauter, Martin Nocker, Florian Merkle, and Pascal Schöttle. "On the Effect of Adversarial Training Against Invariance-based Adversarial Examples", In The 8th International Conference on Machine Learning Technologies (ICMLT), 2023. https://doi.org/10.1145/3589883.3589891
Widmann, T., Merkle, F., Nocker, M., & Schöttle, P. (2023). Pruning for Power: Optimizing Energy Efficiency in IoT with Neural Network Pruning. In L. Iliadis, I. Maglogiannis, S. Alonso, C. Jayne, & E. Pimenidis (Hrsg.), Engineering Applications of Neural Networks (S. 251–263). Springer Nature Switzerland. doi: 10.1007/978-3-031-34204-2_22
Florian Merkle, Maximilian Samsinger, and Pascal Schöttle. "Pruning in the Face of Adversaries", In The 21st International Conference on Image Analysis and Processing (ICIAP), 2022
Daniel Aumayr and Pascal Schöttle. "U Can't (re)Touch This - A Deep Learning Approach for Detecting Image Retouching", In The 21st International Conference on Image Analysis and Processing (ICIAP), 2022
Maximilian Samsinger, Florian Merkle, Pascal Schöttle, and Tomas Pevny. "When Should You Defend Your Classifier? - A Game-theoretical Analysis of Countermeasures against Adversarial Examples", In The 12th Conference on Decision and Game Theory for Security (GameSec), 2021
Nora Hofer, Pascal Schöttle, Alexander Riezler, and Sebastian Stabinger. "Adversarial Examples Against a BERT ABSA Model –Fooling Bert With L33T, Misspellign, and Punctuation," In International Conference on Availability, Reliability and Security (ARES), 2021
Dilger, T., Ploder, C., Haas, W., Schöttle, P., & Bernsteiner, R. (2020). Continuous Planning and Forecasting Framework(CPFF) for Agile Project Management: Overcoming the "Agilefall"-BudgetingTrap. 21st Annual Conference on Information Technology Education (SIGITE '20), October 7-9, 2020, Virtual Event, USA.ACM, Omaha, NB, USA, https://doi.org/10.1145/3368308.3415398
Christian Ploder, Thomas Dilger, and Schöttle Pascal. “Agile Project Budgeting - Teaching the Combination of Agile Project Management and Beyond Budgeting Basics”. In: Proceedings of the 12th International Conference on Education and New Learning Technologies (EDULEARN). 2020
Thomas Baumhauer, Pascal Schöttle, and Matthias Zeppelzauer. "Machine Unlearning: Linear Filtration for Logit-based Classifiers". In: https://arxiv.org/abs/2002.02730
Pascal Schöttle, Alexander Schlögl, Cecilia Pasquini, and Rainer Böhme. "Detecting Adversarial Examples - A Lesson from Multimedia Security" In: 26th European Signal Processing Conference (EUSIPCO), 2018.
Cecilia Pasquini, Pascal Schöttle, and Rainer Böhme. “Decoy Password Vaults: At Least As Hard As Steganography?” In: Proceedings of 32nd International Conference on ICT Systems Security and Privacy Protection (IFIP SEC). 2017, pp. 327–340
Svetlana Abramova, Pascal Schöttle, and Rainer Böhme. “Mixing Coins of Different Quality: A Game-Theoretic Approach”. In: 4th Workshop on Bitcoin and Blockchain Research, in assocation with Financial Cryptography and Data Security 2017. Malta, 2017
Cecilia Pasquini, Pascal Schöttle, Rainer Böhme, Giulia Boato, and Fernando Pèrez- Gonzàlez. “Forensics of High Quality and Nearly Identical JPEG Image Recompression”. In: ACM Information Hiding and Multimedia Security Workshop. Vigo, Galicia, Spain, 2016, pp. 11–21
Matthias Carnein, Pascal Schöttle, and Rainer Böhme. “Telltale Watermarks for Counting JPEG Compressions”. In: Proceedings of IS&T Electronic Imaging: Media Watermarking, Security, and Forensics 2016. San Francisco, CA, 2016
Matthias Carnein, Pascal Schöttle, and Rainer Böhme. “Forensics of High-Quality JPEG Images with Color Subsampling”. In: IEEE International Workshop on Information Forensics and Security (IEEE WIFS). Rome, Italy, 2015
Matthias Kirchner, Pascal Schöttle, and Christian Riess. "Thinking beyond the block: block matching for copy-move forgery detection revisited." Media Watermarking, Security, and Forensics 2015. Vol. 9409. International Society for Optics and Photonics, 2015.
Matthias Carnein, Pascal Schöttle, and Rainer Böhme. “Predictable Rain? Steganalysis of Public-Key Steganography using Wet Paper Codes”. In: ACM Information Hiding and Multimedia Security Workshop. Salzburg, Austria, 2014, pp. 97–108
Erwin Quiring and Pascal Schöttle. “On the Combination of Randomized Thresholds and Non-Parametric Boundaries to Protect Digital Watermarks against Sensitivity Attacks”. In: ACM Information Hiding and Multimedia Security Workshop. Salzburg, Austria, 2014, pp. 41–48
Aron Laszka, Benjamin Johnson, Pascal Schöttle, and Rainer Böhme. “Managing the Weakest Link: A Game-Theoretic Approach for the Mitigation of Insider Threats”. In: Computer Security (ESORICS). Ed. by Jason Crampton, Sushil Jajodia, and Keith Mayes. Vol. 8134. Lecture Notes in Computer Science. Berlin Heidelberg: Springer, 2013, pp. 273– 290
Pascal Schöttle, Aron Laszka, Benjamin Johnson, Jens Grossklags, and Rainer Böhme. “A Game-Theoretic Analysis of Content-adaptive Steganography with Independent Embed- ding”. In: 21st European Signal Processing Conference (EUSIPCO). Marrakech, Morocco: IEEE, 2013, pp. 1–5
Pascal Schöttle, Stefan Korff, and Rainer Böhme. “Weighted Stego-Image Steganalysis for Naive Content-Adaptive Embedding”. In: IEEE International Workshop on Information Forensics and Security (IEEE WIFS). Best paper award. Tenerife, Spain, 2012, pp. 193– 198
Pascal Schöttle and Rainer Böhme. “A Game-Theoretic Approach to Content-Adaptive Steganography”. In: Information Hiding (14th International Conference). Ed. by Matthias Kirchner and Dipak Ghosal. Vol. 7692. Lecture Notes in Computer Science. Berlin Heidel- berg: Springer, 2012, pp. 125–141
Benjamin Johnson, Pascal Schöttle, and Rainer Böhme. “Where to Hide the Bits?” In: Decision and Game Theory for Security. Ed. by Jens Grossklags and Jean Walrand. Vol. 7638. Lecture Notes in Computer Science. Berlin Heidelberg: Springer, 2012, pp. 1–17

Presentation of a paper at a conference, workshop or seminar

Schöttle, P. (2025, August 16). CyberSecurity IoT [Invited Talk]. Euregio Days Forum Alpbach, Alpbach, Austria.
Schöttle, P. (2025, May 8). Josef Ressel Centre for Security Analysis of IoT Devices [Conference Presentation]. FFH2025 18. Forschungsforum der österreichischen Fachhochschulen, Vienna, Austria.
Schöttle, P. (2025, February 20). The Cyber Resilience Act and its Meaning for Cybersecurity Vendors [Invited Talk]. AV-Comparatives Security Summit & Awards, Innsbruck, Austria.
Schöttle, P. (2024, December 17). Praxisvortrag: IoT-Sicherheit [Invited talk]. DigiPro II: Digitalisierung@HTL, St. Pölten, Austria.
Schöttle, P. (2023, October 10). Einblicke in KI Initiativen und Projekte bei INNIO [Invited talk]. Industriellen Vereinigung Tirol - KI in der Tiroler Industrie, Innsbruck, Austria.

Supervised bachelor theses

Kelmer Romed (2025): Anomalie Erkennung in einem LoRaWAN Netzwerk
Buchmann Mira (2025): State-sponsored Actors and Their Implications for Web Application Security
Gritsch Lukas (2025): Persönliches Trainingssystem für Luftgewehrschützen:innen
Windisch Jakob (2025): Comparative Analysis of Self-Supervised Learning Algorithms for Effective Data Labeling and Performance Improvements in Computer Vision Models
Brandacher Daniel (2025): Evaluierung wesentlicher Einflussfaktoren bei der Implementierung eines lokalen Open Source Large Language Models
Ladner Adrian (2025): Reinforcement Learning für die Anomalieerkennung in Netzwerken
Kalupa Aaron Joel (2025): Gamifizierte IT-Sicherheit: Design, Umsetzung und Evaluation einer webbasierten Lernplattform
Jarosch Simon (2024): From Zero to h3r0 Creating a Web Application Security Playbook for Students
Müller Sandra (2024): Analyse der Integration von Securability und deren Einfluss auf die Arbeitsumgebung in österreichischen IT-Unternehmen
Witsch Phillipp (2024): Optimierung der Operationsplanung mit Hilfe von Machine Learning in den Tirol Kliniken
Genctan Pascal (2024): Effiziente Bilderkennung in der Pharmaproduktion durch Supervised Learning
Jauck Nicholas (2024): Screenshot - resistente digitale Wasserzeichen für soziale Netzwerke
Burns-Balog Jana Madison (2024): Towards More Secure Databases : Developing an Adaptive Deep Forest -Based SQL Injection Detection Tool
Gerges Youssef (2023): Web Application Security Education
Schweitzer Stefan (2023): Container Orchestrierungen: Eine qualitative Erhebung zu aktuellen Sicherheitsmaßnahmen und Best Practices
Stöbich Roman (2023): Machine Learning als Methode zur Anomaliedetektion in drahtlosen Lichtsteuersystemen
Payr Patrik (2023): Cyber-Sicherheit für Smartphones: Angriffsvektoren und Container-Anwendungen im Android Betriebssystem als Schutzmaßnahme
Russold Markus (2023): On The Usage Of Deep Learning To Improve License Plate Recognition Quality In Post-Processing Based On A Continual Learning Approach
Gangl Katharina (2023): Ultimateberechnung von Hagelschäden in Österreich mithilfe von Machine Learning
Suntinger Fabian (2023): Abschätzung der Arbeitszeit für zusätzliche Ingenieurleistungen für Balance-of-Plant-/Anlagenbau- und Sonderaufträge bei INNIO Jenbacher
Rauter Roland (2022): Trainieren eines CNN mithilfe von Invariance-Based Adversarial Examples
Stockinger Rene (2022): Universal Adversarial Perturbations als Angriff gegen Gesichtserkennung
Mösl Michael (2022): On-Device Modell Optimierung von Machine-Learning Algorithmen auf mobilen Geräten
Hüttl Mathis (2022): Welche Anforderungen hat eine Software zur Bekämpfung von Fake News im Internet und wie kann diese umgesetzt werden?
Köll Julian (2022): Cybersecurity in Computerspielen - Sicherheitsaspekte von Offline- und Online-Games
Griesser Daniel (2022): State-of-the-art Algorithmen zur Generierung von Adversarial Examples - ein Vergleich
Gugler Anton (2022): Material demand forecasting : Ein Vergleich statistischer, Machine Learning und Deep Learning Methoden.
Temizkan Abdulkadir (2022): Evaluierung von Social Engineering Methoden
Mirocha Tobias (2021): PKI-basierte E-Mail-Verschlüsselung und -Signatur
Eichhorn Philipp (2021): Few-Shot Learning am Beispiel automatisierter Rechnungsklassifikation
Thurner Maximilian (2021): Entwicklung eines Konzepts zur Steigerung der Passwortsicherheit und dessen Anwendung in einer Sicherheitsanalyse
Divković Marijan (2021): Making Machine Learning Accessible for SMEs: Framework Requirements and Clustering Prototype
Klingenschmid Lukas (2021): Detektion von Laser-Range Finder Messpulsen unter Verwendung von Machine Learning Algorithmen
Lerch Judith (2021): Machine Learning zur Vorhersage von Zeitaufwand im Kontext von INNIO Jenbacher
Aumayr Daniel (2021): Automatische Erkennung von Bildmanipulationen durch mobile Applikationen mit einem CNN

Supervised master theses

Sandmayr Maximilian (2024): Video Analysis in Supermarkets to Prevent Theft
Kaltenstadler Marco (2024): Towards Efficient and Secure Update Management Mechanisms for IoT Devices: An Analysis of the Matter Standard
Moog Patrick (2022): Fooling Neural Networks for Age-/Gender- and Emotion-Prediction with Adversarial Patches
Sirbu Mihaela Roxana (2021): Humans vs. CNNs: susceptibility to invariance and sensitivity based adversarial examples
Weber David (2021): How much pruning is too much? The effects of neural network pruning on machine learning explainability
Hofer Nora (2020): On the Robustness of a BERT Model for ABSA against Input Level Adversarial Examples
Merkle Florian (2020): The Impact of Network Pruning on the Adversarial Robustness of Deep Neural Networks
Bleckmann Carl (2020): What‘s next for IoT Forensics? A Proposal for Integration of Multimedia Forensic Methods to IoT Forensic Concepts
Muhr Valentin (2019): Data Deletion in Deep Learning Networks