Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    SWaTEval: An Evaluation Framework for Stateful Web Application Testing
    ( 2023)
    Borcherding, Anne
    ;
    Penkov, Nikolay
    ;
    Giraud, Mark Leon
    ;
    Beyerer, Jürgen
    Web applications are an easily accessible and valuable target for attackers. Therefore, web applications need to be examined for vulnerabilities. Modern web applications usually behave in a stateful manner and hence have an underlying state machine that determines their behavior based on the current state. To thoroughly test a web application, it is necessary to consider all aspects of a web application, including its internal states. In a blackbox setting, which we presuppose for this work, however, the internal state machine must be inferred before it can be used for testing. For state machine inference it is necessary to choose a similarity measure for web pages. Some approaches for automated blackbox stateful testing for web applications have already been proposed. It is, however, unclear how these approaches perform in comparison. We therefore present our evaluation framework for stateful web application testing, SWaTEval. In our evaluation, we show that SWaTEval is able to repr oduce evaluation results from literature, demonstrating that SWaTEval is suitable for conducting meaningful evaluations. Further, we use SWaTEval to evaluate various approaches to similarity measures for web pages, including a new method based on the euclidean distance that we propose in this paper. These similarity measures are an important part of the automated state machine inference necessary for stateful blackbox testing. We show that the choice of similarity measure has an impact on the performance of the state machine inference regarding the number of correctly identified states, and that our newly proposed similarity measure leads to the highest number of correctly identified states.
  • Publication
    Sensitivity enhanced glucose sensing by return-path Mueller matrix ellipsometry
    ( 2023)
    Chen, Chia Wei
    ;
    Hartrumpf, Matthias
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Diabetes is a worldwide public health problem. According to the survey of the Robert Koch Institute, in Germany, at least 7.2 percent population (aged between 18 to 79 years) have diabetes. Therefore, the demand for glucose monitoring is increasing, especially for non-invasive glucose monitoring technology. In this work, we proposed a novel method to enhance the sensitivity of glucose monitoring by return-path ellipsometry with a quarter-wave plate and mirror. The coaxial design improves the sensitivity and reduces the complexity of optical system alignment by means of a fixed quarter-wave plate. The proposed system showed higher sensitivity compared to the transmission configuration.
  • Publication
    The Bandit’s States: Modeling State Selection for Stateful Network Fuzzing as Multi-armed Bandit Problem
    ( 2023)
    Borcherding, Anne
    ;
    Giraud, Mark Leon
    ;
    Fitzgerald, Ian
    ;
    Beyerer, Jürgen
    Network interfaces of Industrial Control Systems are a common entry point for attackers, and thus need to be thoroughly tested for vulnerabilities. One way to perform such tests is with network fuzzers, which randomly mutate network packets to induce unexpected behavior and vulnerabilities. Highly stateful network protocols pose a particular challenge to fuzzers, since a fuzzer needs to be aware of the states in order to find deep vulnerabilities. Even if a fuzzer is aware of the states of a stateful network protocol, there are still several challenges to overcome. The challenge we focus on is deciding which state to test next. To make this decision, the fuzzer needs to strike a balance between exploiting known states and exploring states not yet tested. We propose to model this exploration versus exploitation dilemma using a Multi-armed Bandit. In this work, we present two modeling approaches and preliminary experiments. We choose to model the state selection problem with (I) a stochastic Multi-armed Bandit, and (II) an adversarial Multi-armed Bandit. The latter takes into account that coverage can only be discovered once, and that the underlying reward probability therefore decreases over time. Although the adversarial Multi-armed Bandit models the state selection problem more accurately, our experiments show that both approaches lead to statistically indistinguishable fuzzer performance. Furthermore, we show that the baseline fuzzer AFLNet leads to significantly better results in terms of coverage. Building on these unintuitive preliminary results, we aim to investigate the behavior of the agents in more detail, to include additional modeling approaches, and to use additional Systems under Test for the evaluation.
  • Publication
    Simulation study and experimental validation of a neural network-based predictive tracking system for sensor-based sorting
    ( 2023)
    Maier, Georg
    ;
    Reith-Braun, Marcel
    ;
    Bauer, Albert
    ;
    Gruna, Robin
    ;
    Pfaff, Florian
    ;
    Kruggel-Emden, Harald
    ;
    Längle, Thomas
    ;
    Hanebeck, Uwe D.
    ;
    Beyerer, Jürgen
    Sensor-based sorting offers cutting-edge solutions for separating granular materials. The line-scanning sensors currently in use in such systems only produce a single observation of each object and no data on its movement. According to recent studies, using an area-scan camera has the potential to reduce both characterization and separation error in a sorting process. A predictive tracking approach based on Kalman filters makes it possible to estimate the followed paths and parametrize a unique motion model for each object using a multiobject tracking system. While earlier studies concentrated on physically-motivated motion models, it has been demonstrated that novel machine learning techniques produce predictions that are more accurate. In this paper, we describe the creation of a predictive tracking system based on neural networks. The new algorithm is applied to an experimental sorting system and to a numerical model of the sorter. Although the new approach does not yet fully reach the achieved sorting quality of the existing approaches, it allows the use of the general method without requiring expert knowledge or a fundamental understanding of the parameterization of the particle motion model.
  • Publication
    Detecting Tar Contaminated Samples in Road-rubble using Hyperspectral Imaging and Texture Analysis
    ( 2023)
    Bäcker, Paul
    ;
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Polycyclic aromatic hydrocarbons (PAH) containing tar-mixtures pose a challenge for recycling road rubble, as the tar containing elements have to be extracted and decontaminated for recycling. In this preliminary study, tar, bitumen and minerals are discriminated using a combination of color (RGB) and Hyperspectral Short Wave Infrared (SWIR) cameras. Further, the use of an autoencoder for detecting minerals embedded inside tar- and bitumen mixtures is proposed. Features are extracted from the spectra of the SWIR camera and the texture of the RGB images. For classification, linear discriminant analysis combined with a k-nearest neighbor classification is used. First results show a reliable detection of minerals and positive signs for separability of tar and bitumen. This work is a foundation for developing a sensor-based sorting system for physical separation of tar contaminated samples in road rubble.
  • Publication
    Optimizing Fine-Grained Fungi Classification for Diverse Application-Oriented Open-Set Metrics
    ( 2023)
    Wolf, Stefan
    ;
    Beyerer, Jürgen
    Fine-grained fungi species classification is an important task to support distinguishing edible and poisonous fungi and thus, reducing the risk of accidental poisoning. Therefore, the FungiCLEF 2023 challenge seeks to find the best solution for this task considering multiple metrics with each having a different application in focus like e.g., a low confusion of edible and poisonous fungi. We propose a method to approach the different metrics by exploiting modern deep learning networks, strong data augmentation and class-balanced training. The challenge assumes an open-set scenario which includes unknown classes during evaluation which we identify by a confidence thresholding approach. With our method, we achieved the 2nd place in the challenge with good scores across all metrics. Code is available at: https://github.com/wolfstefan/fungi2023.
  • Publication
    Usability for Data Sovereignty - Evaluation of Privacy Risk Quantification Interfaces
    ( 2023)
    Appenzeller, Arno
    ;
    Balduf, Falk
    ;
    Beyerer, Jürgen
    Digital medical data is becoming widely available through the ongoing digitization efforts in the medical sector. This also leads to more personal health data available for secondary usage like medical research. A common way to collect medical data in a privacy compliant way is through the informed consent of the affected person. While consent forms are typically paper-based, in the last years the concept of digital consent is becoming more and more common. Still, such consent forms can be very complex and overwhelming for the patient. For example, it can be hard to estimate the personal privacy impact when sharing data of rare medical conditions and user interfaces for data sharing need to be designed carefully. Privacy Risk Quantifications (PRQ) and Dynamic Consent (DC) are two tools to help patients to make a consent decision and choose the data for sharing. However, interfaces for those two technologies are not trivial to design. This paper develops two new interface variants with a focus on design guidelines and best practices for usability. To evaluate the new designs, a user study was conducted which shows improved usability in comparison to an existing interface. The Base variant is a prototype that was previously developed as a technical demonstrator for DC and PRQ without requirements for usability. The three interfaces are evaluated in a user study, which shows that the usability focus of the newer variants leads to a better rating by the test subjects compared to the basic prototype. It can also be seen that interfaces with more detailed explanations and a focus on visual comprehensive and compelling interfaces get a better usability score compared to the pure technical versions. Finally, it can be seen that interfaces for those technologies are ideally developed in an iterative design development cycle.
  • Publication
    Regression-based Age Prediction of Plastic Waste using Hyperspectral Imaging
    ( 2023)
    Kronenwett, Felix
    ;
    Klingenberg, Pia Charlotte Amaryllis
    ;
    Maier, Georg
    ;
    Längle, Thomas
    ;
    Metzsch-Zilligen, Elke
    ;
    Beyerer, Jürgen
    In order to enable high quality recycling of polypropylene (PP) plastic, additional classification and separation into the degree of degradation is necessary. In this study, different PP plastic samples were produced and degraded by multiple extrusion and thermal treatment. Using near infrared spectroscopy, the samples were examined and regression models were trained to predict the degree of aging. The models of the multiple extruded samples showed high accuracy, despite only minor spectral changes. The accuracy of the models of the thermally aged samples varied with the design of the training set due to the non-linear aging process, but showed sufficient accuracy in prediction.
  • Publication
    KI-Systeme schützen, Missbrauch verhindern
    ( 2022-03-24)
    Beyerer, Jürgen
    ;
    Müller-Quade, Jörn
    Künstliche Intelligenz wird bereits in einer Vielzahl von gesellschaftlichen Bereichen eingesetzt, sei es im Gesundheitsbereich, in der Arbeitswelt, im Straßenverkehr oder in öffentlichen Räumen. Trotz der vielfältigen Chancen, die die KI-Technologie mit sich bringt, wie etwa eine verbesserte Gesundheitsversorgung oder eine attraktive, individuelle Arbeitsplatzgestaltung, sollte das Potenzial für Missbrauch von KI-Systemen nicht aus den Augen verloren und realistisch eingeschätzt werden. Auf diese Weise können frühzeitig passende Schutzmaßnahmen gegen missbräuchliche Angriffe strategisch ergriffen werden. Die Expertinnen und Experten unter Federführung der beiden Arbeitsgruppen Lebensfeindliche Umgebungen sowie IT-Sicherheit, Privacy, Recht und Ethik der Plattform Lernende Systeme gehen im Whitepaper der Frage nach, welche Schritte unternommen werden sollten, um Missbrauch von KI-Systemen mit geeigneten Maßnahmen vorzubeugen. Dies vorrangig unter technologischen Aspekten. Hierzu empfehlen sie, Szenarien durchzudenken, um frühzeitig Einfallstore aufzudecken und daraus erforderliche Schutzvorkehrungen abzuleiten, die eingebettet in einer Gesamtstrategie Missbrauch verhindern können. Zur Veranschaulichung werden die theoretischen Überlegungen in realistischen Anwendungsszenarien aus dem Bereich Gesundheit, Freizeit, Mobilität oder Arbeitswelt eingebettet. Diese machen in der in der Gegenüberstellung zwischen „worst case“ und „best case“ deutlich, welchen Ausgang geeignete Maßnahmen im konkreten Missbrauchsfall letztlich begünstigen - dies im Sinne einer sicheren und zuverlässigen KI-Technologie.