Uncovering the social impact of digital steganalysis tools applied to cybercrime investigations: a European Union perspective

Author:

Nicolás-Sánchez AlejandroORCID,Castro-Toledo Francisco J.

Abstract

Abstract Background European Union (EU) research on cybersecurity is actively developing more efficient digital steganalysis techniques aimed at uncovering hidden online illegal content in apparently legitimate multimedia files. Beyond issues such as the design, effectiveness and functionality of the technology, this paper addresses the recently raised concern of societal impact, which refers to the influence, consequences, or effects, whether expected or not, that a particular action, policy, or technological advance has on society as a whole or on different segments of society. These impacts can be broad and multifaceted, encompassing economic, social, cultural, environmental and ethical dimensions, amongst others. Aim The aim of this article is to take an exploratory look at the societal challenges and benefits associated with the use of digital steganalysis tools in cybercrime investigations in EU member states, adopting a dual mixed-methods perspective. Methods First, a systematic review of the scientific literature published within 2017–2023, focusing on the societal dimension of steganalysis tools, including peer reviewed journal and conference papers on steganalysis and crime (N = 55) was carried out. For the second part of the paper, two nominal group discussions were conducted with experts from Law Enforcement Agencies (LEAs): the first on societal benefits (N = 7), the second on societal challenges (N = 6). These consensus-building discussions aimed to identify, quantitatively assess and rank the various challenges and potential social benefits associated with the use of digital steganalysis tools in police investigations. Results Findings reveal a widespread oversight in addressing the social impact dimension by tool designers on academic papers, especially regarding societal acceptance issues. The expert-citizens argued for stakeholders and public awareness of both risks and benefits of steganalysis tools. Conclusions This study highlights the current need to consider not only the technological aspects, but also the profound social dimension arising from the use of these tools, such as public awareness of cybercrime and the ethical design and use of digital crime investigation tools. Understanding and evaluating societal impacts is essential for making informed decisions, shaping policies, and addressing the needs and concerns of diverse stakeholders in various domains. This multidisciplinary approach is crucial to achieving a more balanced and comprehensive understanding of the impact of digital steganalysis tools in the field of digital criminal investigation.

Funder

H2020 Security

Publisher

Springer Science and Business Media LLC

Reference86 articles.

1. Almeida, J. R., Fajarda, O., & Oliveira, J. L., et al. (2020). File forgery detection using a weighted rule-based system. In A. Arampatzis (Ed.), Experimental IR meets multilinguality, multimodality, and interaction. CLEF 2020 lecture notes in computer science. Cham: Springer International Publishing.

2. Araujo, I. I., & Kazemian, H. (2020). Improving steganographic capacity using distributed steganography over BMP. Multimedia Tools and Applications, 79(35–36), 26181–26195. https://doi.org/10.1007/s11042-020-09298-3

3. Arshad, H., Jantan, A., & Abiodun, O. (2018). Digital Forensics: Review of issues in scientific validation of digital evidence. Journal of Information Processing Systems, 14(2), 346–376. https://doi.org/10.3745/jips.03.0095

4. Athanasiadou, E., Geradts, Z., & Van Eijk, E. (2018). Camera recognition with deep learning. Forensic Sciences Research, 3(3), 210–218. https://doi.org/10.1080/20961790.2018.1485198

5. Aumayr, D., & Schöttle, P. (2022). U can’t (re)touch this—a deep learning approach for detecting image retouching. In S. Sclaroff, C. Distante, M. Leo, G. M. Farinella, & F. Tombari (Eds.), Image analysis and processing—ICIAP 2022. Cham: Springer International Publishing.

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