An empirical approach for digital currency forensics

The banknote manufacturing industry is shrouded in secrecy, fundamental mechanics of security components are closely guarded trade secrets. Currency forensics is the application of systematic methods to determine authenticity of questioned currency. However, forensic analysis is a difficult task requiring specially trained examiners, the most important challenge is automating the analysis process reducing human error and time. In this study, an empirical approach for automated currency forensics is formulated and a prototype is developed. A two parts feature vector is defined comprised of color features and texture features. Finally the note in question is classified by a Feedforward Neural Network (FNN) and a measurement of the similarity between template and suspect note is output.