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
Prof. Dr. Pascal Schöttle

Pascal is a professor for IT Security and Machine Learning at the department Digital Business & Software Engineering at MCI - The Entrepreneurial School in Innsbruck.


Professional work experience
  • 06/2022 - present
    Professor - MCI - The Entrepreneurial School, Austria
  • 10/2020 - 06/2022
    Associate Professor - MCI - The Entrepreneurial School, Austria
  • 10/2018 - 09/2020
    Lecturer - MCI - The Entrepreneurial School, Austria
  • 03/2015 - 09/2018
    Post-Doc - University of Innsbruck, Austria
  • 09/2014 - 02/2015
    Post-Doc - University Münster, Deutschland
  • 02/2013 - 04/2013
    Visiting Scholar - Pennsylvania State University, USA
  • 09/2011 - 08/2014
    Scientific Staff Member (Doctoral Candidate) - University Münster, Germany
  • 07/2010 - 06/2011
    Scientific Student Assistent - Ruhr-University Bochum, Deutschland
Education
  • 10/2011 - 07/2014
    Ph. D. (Dr. rer. nat) - University Münster, Germany
    Computer Science
  • 04/2008 - 06/2011
    Master of Science - Ruhr-University Bochum, Germany
    IT Security / Networks and Systems
  • 10/2003 - 02/2008
    Bachelor of Science - University of Duisburg-Essen, Germany
    Mathematical Engineering
Teaching (faculty internships etc.)
  • 01/2020 - present
    MCI - The Entrepreneurial School, Austria
    Smart Systems & Machine Learning
  • 01/2020 - present
    MCI - The Entrepreneurial School, Austria
    IT Security
  • 01/2020 - 12/2022
    MCI - The Entrepreneurial School, Austria
    Integrated Overall Project
  • 01/2019 - present
    MCI - The Entrepreneurial School, Austria
    Mathematics for Software Engineering
  • 01/2019 - 12/2022
    MCI - The Entrepreneurial School, Austria
    Algorithms & Data Structures
  • 01/2019 - present
    MCI - The Entrepreneurial School, Austria
    Management Information Systems
  • 01/2019 - 12/2021
    MCI - The Entrepreneurial School, Austria
    Data Science
  • 01/2019 - present
    MCI - The Entrepreneurial School, Austria
    Security for Smart Technologies
  • 01/2018 - present
    MCI - The Entrepreneurial School, Austria
    Logic & Computability
  • 01/2017 - 12/2018
    University of Innsbruck, Austria
    Communication Technology and Computer Networks
  • 01/2016 - 12/2018
    University of Innsbruck, Austria
    Information Security II
  • 01/2015 - 12/2018
    University of Innsbruck, Austria
    Introduction to Computer Engineering
  • 01/2015 - 12/2018
    University of Innsbruck, Austria
    Computer Networks and Internet Technology
  • 01/2012 - 12/2014
    University Münster, Germany
    Multimedia Security
  • 01/2012 - 12/2014
    University Münster, Germany
    Advanced Cryptology
Practice related research and developmental project
  • 07/2023 - present
    Head of Research Center - CDG - Christian Doppler Forschungsgesellschaft
    Josef Ressel Center for Security Analysis of IoT Devices
  • 04/2021 - 12/2023
    Principal Investigator (@ MCI) - FFG - The Austrian Research Promotion Agency
    SMiLE - Secure Machine Learning Applications with Homomorphically Encrypted Data
  • 01/2019 - 06/2023
    Principal Investigator - FWF - Austrian Science Fund
    Game Over Eva(sion): Securing Deep Learning with Game Theory Keywords: Deep Learning, Security, Game Theory, Evasion Attacks
  • 01/2017 - 12/2019
    Project Leader - Forschungsförderungsmittel der Nachwuchsförderung 2017 (University Innsbruck)
    “Forensic Analysis of Scanned Text Documents" (FASTDoc).
Competitive Research Award
  • 11/2012 - 11/2012
    IEEE International Workshop on Information Forensics and Security
    Best Student Paper Award
  • 09/2008 - 09/2008
    CAST - Competence Center for Applied Security Technology
    2nd place at CAST Förderpreis in IT security,
Further education & Training
  • 2019 - 2019
    MCI - The Entrepreneurial School
    Blended Learning 1-2-3
  • 2018 - 2018
    MCI - The Entrepreneurial School
    Sakai Advanced
  • 2018 - 2018
    MCI - The Entrepreneurial School
    Adobe Connect Advanced
  • 2018 - 2018
    MCI - The Entrepreneurial School
    Sakai Basic
  • 2018 - 2018
    MCI - The Entrepreneurial School
    Adobe Connect Basics
Other
  • 03/2015 - 03/2015
    FFG Relocation Grant, for the relocation from Germany to Austria.
  • 03/2013 - 03/2013
    ONR Visiting Researcher Grant, funding the research visit at the Pennsylvania State University, US.
Positions in professional/academic societies
  • 01/2018 - 12/2018
    Programm Committee Co-Chair - ACM Information Hiding & Multimedia Security 2018
  • 01/2018 - 12/2018
    Programm Committee Member - IEEEE International Conference on Acoustics, Speech, and Signal Processing
  • 01/2015 - 12/2020
    Program Committee Member - ACM Information Hiding & Multimedia Security
  • 01/2015 - 12/2018
    Programm Committee Member - International Workshop on Digital-forensics and Watermarking
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
  • Egger, C., Janetschek, M., Nocker, M., Merkle, F., & Schöttle, P. (2025). A Meta Device Model for Automated Security Assessment of Internet of Things Devices. Proceedings of the 15th International Conference on the Internet of Things, 59–67. https://doi.org/10.1145/3770501.3770509
  • Nocker, M., Merkle, F., Schöttle, P., & Janetschek, M. (2026). Protecting Privacy in IoT-Based Deep Learning: State-of-the-Art Methods and Challenges. In M. Manulis (Ed.), Applied Cryptography and Network Security Workshops (pp. 163–184). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-01799-4_10
  • 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
  • Haselwanter Rusty (2025): Zero-Trust-Inspired Network Segmentation for SMEs: A Practical Guide to Securing Operational Technology Bachelor Thesis
  • Kelmer Romed (2025): Anomalie Erkennung in einem LoRaWAN Netzwerk
  • Buchmann Mira (2025): State-sponsored Actors and Their Implications for Web Application Security
  • Astner Mathias (2025): Smart but Secure? A Pentest of the Nuki Smart Lock
  • 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
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