Publications

  • Semantic Robustness Probing via Inpainting: An Interactive Tool for Data Augmentation for Safety-Critical Object Detection
    Nico Steckhan, Krutarth Prajapati, Weijia Shao, Jonas Heinle & Silvia Vock
    Machine Learning and Knowledge Discovery in Databases: Demo Track, May 26, 2026
    We present SemProbe, a tool for semantic robustness probing: users upload deployment images, create masks manually or automatically, select operational design domain-derived factors (or custom prompts), and run diffusion-based controlled inpainting
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  • Stylized Synthetic Augmentation Further Improves Corruption Robustness
    Georg Siedel, Rojan Regmi, Abhirami Anand, Weijia Shao, Silvia Vock & Andrey Morozov
    Proceedings of the 21st International Conference on Computer Vision Theory and Applications (VISAPP 2026) - Volume 2, pages 31-42, March 10, 2026
    A data augmentation pipeline consisting of synthetic images, neural style transfer and TrivialAugment is developed that enables state of the art robustness on small-to-medium scale image classification datasets.
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  • Improving and Evaluating the Corruption Robustness of Image Classifiers Using Random p-Norm Noise
    Georg Siedel, Weijia Shao, Silvia Vock & Andrey Morozov
    Communications in Computer and Information Science ((CCIS, volume 2548)): Revised Selected Papers of VISAPP 2024, February 10, 2026
    On using random noise sampled from arbitrary p-norms as a data augmentation strategy for the assumption-free evaluation and the training of robust image classification models.
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  • Combined Image Data Augmentations Diminish the Benefits of Adaptive Label Smoothing
    Georg Siedel, Ekagra Gupta, Weijia Shao, Silvia Vock & Andrey Morozov
    DAGM German Conference on Pattern Recognition (GCPR 2025), September 26, 2025
    Can adaptive label smoothing be applied with advanced data augmentation strategies to improve accuracy and robustness of vision models?
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  • The need for systematic approaches in risk assessment of safety-critical AI-applications in machinery
    Franziska Wolny, Silvia Vock, Rasmus Adler, Taras Holayad
    Proceedings of the European Safey and Reliability Conference (ESREL), Stavanger/Norway, June 15, 2025
    The integration of artificial intelligence (AI) into safety-critical machinery applications in industrial environments presents substantial challenges for conformity assessment and safety certification.
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  • Dynamic Risk Assessment for Human-Robot Collaboration Using a Heuristics-based Approach
    Georgios Katranis, Frederik Plahl, Joachim Grimstadt, Ilshat Mamaev, Silvia Vock, Andrey Morozov
    35th European Safety and Reliability Conference (ESREL 2025), January 21, 2025
    Human-robot collaboration (HRC) introduces significant safety challenges, particularly in protecting human operators working alongside collaborative robots (cobots). While current ISO standards emphasize risk assessment and hazard identification, these procedures are often insufficient for addressing the complexity of HRC environments, which involve numerous design factors and dynamic interactions.
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  • From Classical to Advanced Risk Methods: Demonstrator for Industrial Cyber-Physical Systems
    Andrey Morozov, Tagir Fabarisov, Silvia Vock, Thorben Schey, Artur Karimov, Georg Siedel, Joachim Grimstad, Arne Sonnenburg, Thomas Mossner
    35th European Safety and Reliability Conference (ESREL 2025), January 14, 2025
    Modern industrial Cyber-Physical Systems (CPS) exhibit high levels of reconfigurability and heterogeneity, posing significant challenges for risk assessment in dynamic environments. Traditional risk assessment methods, originally developed for simpler systems
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  • A Time-series Data Generation Tool for Risk Assessment of Robotic Applications
    Yuliang Ma, Apurv Patel, Don Kurian, Julien Siebert, Silvia Vock, Andrey Morozov
    35th European Safety and Reliability Conference (ESREL 2025), January 12, 2025
    Robotic systems increasingly rely on artificial intelligence (AI) to enhance their capabilities in performing complex tasks across various domains. The development and evaluation of AI systems usually require high-quality datasets
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  • A practical approach to evaluating the adversarial distance for machine learning classifiers
    Siedel, G., Gupta, E., Morozov, A.
    Proceedings of the ASME 2024 International Mechanical Engineering Congress and Exposition (IMECE), November 17, 2024
    Adversarial distance is a highly informative measure of robustness that enjoys little attention in the adversarial robustness domain. We built a practical attack algorithm to evaluate this measure more effectively than implementations in the popular Adversarial Robustness Toolbox (ART). We also combine this upper-bound estimator with the popular lower-bound estimation method CLEVER, showing CLEVERs inability to calculate a correct lower bound in many cases.
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  • Bewertung von KI-basierten Personenerkennungsalgorithmen im Gefahrenbereich von Baumaschinen
    Volker Waurich, Johannes Taesch, Georg Siedel, Franziska Wolny
    10. Fachtagung Baumaschinentechnik, Dresden, September 09, 2024
    Wie kann KI dazu beitragen kann, die Sicherheit auf Baustellen zu erhöhen? Potenziale und Herausforderungen dieser Technologie.
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