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Authorship verification (AV) is a research subject in the field of digital text forensics that concerns itself with the question, whether two documents have been written by the same person. During the past two decades, an increasing number of proposed AV approaches can be observed. However, a closer look at the respective studies reveals that the underlying characteristics of these methods are rarely addressed, which raises doubts regarding their applicability in real forensic settings. The objective of this paper is to fill this gap by proposing clear criteria and properties that aim to improve the characterization of existing and future AV approaches. Based on these properties, we conduct three experiments using 12 existing AV approaches, including the current state of the art. The examined methods were trained, optimized and evaluated on three self-compiled corpora, where each corpus focuses on a different aspect of applicability. Our results indicate that part of the methods are able to cope with very challenging verification cases such as 250 characters long informal chat conversations (72.7% accuracy) or cases in which two scientific documents were written at different times with an average difference of 15.6 years (> 75% accuracy). However, we also identified that all involved methods are prone to cross-topic verification cases.

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Data corpora are very important for digital forensics education and research. Several corpora are available to academia; these range from small manually-created data sets of a few megabytes to many terabytes of real-world data. However, different corpora are suited to different forensic tasks. For example, real data corpora are often desirable for testing forensic tool properties such as effectiveness and efficiency, but these corpora typically lack the ground truth that is vital to performing proper evaluations. Synthetic data corpora can support tool development and testing, but only if the methodologies for generating the corpora guarantee data with realistic properties. This paper presents an overview of the available digital forensic corpora and discusses the problems that may arise when working with specific corpora. The paper also describes a framework for generating synthetic corpora for education and research when suitable real-world data is not available.