A systematic approach for comparing the effectiveness of counterfeit deterrence features in banknotes, credit cards, digital media, etc. was previously presented. That approach built a probabilistic model around the expert identification of the most efficient process by which a counterfeiter can gain sufficient information to replicate a particular feature. We have extended the scope and functionality of that approach to encompass the entire counterfeiting process from the learning phase to the production of counterfeits. The extended approach makes determining the probabilities more straightforward by representing a more detailed model of the counterfeiting process, including many probable counterfeiting scenarios rather than just representing the least costly successful scenario. It uses the counterfeiter's probability of succeeding and level of effort as metrics to perform feature comparisons. As before, these metrics are evaluated for a security feature and presented in a way that facilitates comparison with other security features similarly evaluated. Based on this representation, the cost and laboratory procedures necessary for succeeding may be recovered by a dynamic programming technique. This information may be useful in forensic profiling of potential counterfeiters.
KEYWORDS: Information security, Computer security, Signal processing, Error analysis, Visual process modeling, Applied physics, Monte Carlo methods, Statistical modeling, Analytical research, Statistical analysis
As new counterfeit deterrent features are considered for new document designs, the need exists for evaluating candidate features' performance in a systematic and objective manner. To this end, an application program has been developed that is based on a probabilistic model of the counterfeiting process as determined by interview of experts in the field. The probabilistic approach attempts to capture the variability in understanding and replicating a security feature by representing the steps of the counterfeiting process with a Markov chain, and by maintaining the total cost incurred by the counterfeiter probabilistically. The application provides an estimate of the probability of understanding the security feature given the amount of resources the counterfeiter has at his disposal. The relative effectiveness of security features can be ranked based on this information, and on a scale that also provides information about the resources required of a potential counterfeiter of a note with that feature. An investigation found that the problem of determining the transition probabilities of the Markov chain is well defined. Further, sensitivity analysis shows that the model results are not highly sensitive to variation in model inputs by different users.
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