
FSI: Digital Investigation - Two papers accepted
29 Januar 2025
Our articles on Optimising data set creation in the cybersecurity landscape with a special focus on digital forensics: Principles, characteristics, and use cases and Source Camera Identification - Do we have a gold standard? have been accepted for publication in the renowned Forensics Science International: Digital Investigation Journal.
The first article contains a comprehensive literature review to analyse existing problems with data sets and typical community expectations of data sets, and then presents principles and associated properties and characteristics that should be considered when creating data sets in order to produce high-quality data sets.
The second article introduces a model to classify Source Camera Identification approaches, formally defining three core problem classes—Verification, Identification, and Exploration—while critically evaluating existing methods, exposing limitations in scalability and relevance, and proposing a roadmap for future research.
These two articles will be published in the Forensic Science International: Digital Investigation Journal Volume 52 in March 2025.
Paper I Details:
Title: Optimising data set creation in the cybersecurity landscape with a special focus on digital forensics: Principles, characteristics, and use cases
Authors: Thomas Göbel (Universität der Bundeswehr München), Frank Breitinger (Universität Augsburg), Harald Baier (Universität der Bundeswehr München)
Abstract: Data sets (samples) are important for research, training, and tool development. While the FAIR principles, data repositories and archives like Zenodo and NIST's Computer Forensic Reference Data Sets (CFReDS) enhance the accessibility and reusability of data sets, standardised practices for crafting and describing these data sets require further attention. This paper analyses the existing literature to identify the key data set (generation) characteristics, issues, desirable attributes, and use cases. Although our findings are generally applicable, i.e., to the cybersecurity domain, our special focus is on the digital forensics domain. We define principles and properties for cybersecurity-relevant data sets and their implications for the data creation process to maximise their quality, utility and applicability, taking into account specific data set use cases and data origin. We aim to guide data set creators in enhancing their data sets' value for the cybersecurity and digital forensics field.
Paper II Details:
Title: Source Camera Identification - Do we have a gold standard?
Authors: Samantha Klier (Universität der Bundeswehr München), Harald Baier (Universität der Bundeswehr München)
Abstract: Source Camera Identification (SCI) is vital in digital forensics, yet its most prominent approach, Sensor Pattern Noise (SPN), faces new challenges in the era of modern devices and vast media datasets. This paper introduces the Source Camera Target Model (SCTM) to classify SCI approaches and formally defines three core problem classes: Verification, Identification, and Exploration. For each, we outline key evaluation metrics tailored to practical use cases. Applying this framework, we critically assess recognized SCI methods and their alignment with contemporary needs. Our findings expose significant gaps in scalability, efficiency, and relevance to modern imaging pipelines, challenging the notion of SPN as a gold standard. Finally, we provide a roadmap for advancing SCI research to address these limitations and adapt to evolving technological landscapes.