Optimization of Biomedical Imaging Filters for use in Recaptured Identity Document Classification

As banks and online financial institutions move toward full remote onboarding services, the attack vectors for bad actors increases to include those of recaptured identity documents. This type of fraud opens banking customers to potential crimes of identity theft, as well as causing reputational damage to the institutions involved. In this paper we extend existing research focusing on the use of biomedical imaging filters and their usefulness when classifying recaptured identity documents. We perform a grid search and demonstrate that different filter configurations exist that dramatically reduce the classification error rates compared to those achieved using only the default filter configurations. Ultimately, we are able to achieve an Attack Presentation Classification Error Rate1 (APCER) of 7.6% and a Bona Fide Presentation Classification Error Rate (BPCER) of 5.2% using an ensemble of Random Forest machine learning classifiers trained only on histogram intensity values.