Abstract
Forensics is a science discipline that deals with collecting evidence in crime scene investigation. However if we’re dealing with signatures, the crime scene is the signed paper itself. Therefore, for any kind of investigation, there should be a sample and a master signature to benchmark the similarities and differences. The characteristics of a master signature could easily be identified by forensics techniques, yet it is still infeasible for electronic signatures due to ease of copy-pasting. Through the emerging touchscreen technologies, the features of the signatures could be stealthily extracted and stored while the user is signing. Given these facts, the novelty we put forward in this paper is a feature extraction method using short time Fourier transformations to identify frequencies of a simple master signature. We subsequently presented the spectrogram analysis revealing the differences between the original and fake signatures. Finally a validation method for the analysis of the spectrograms is introduced which resulted in a significant gap between real and fraud signatures for various window sizes.
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Acknowledgment
This work and the contribution were supported by project “SP/2016/2102 - Smart Solutions for Ubiquitous Computing Environments” from FIM, University of Hradec Kralove.
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Alpar, O., Krejcar, O. (2016). Hidden Frequency Feature in Electronic Signatures. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_13
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DOI: https://doi.org/10.1007/978-3-319-42007-3_13
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