51: Development and Evaluation of a Contrastive Learning Framework for Forensic Source Identification

Music City Center 

To interpret the value of forensic evidence resulting from paired item data, the common source identification framework asks: do the items share a common unknown source or come from two different unknown sources? This question can be addressed using a variety of forensic statistics techniques, including the usual Two-Stage, Likelihood Ratio, and Bayes Factor approaches. Contrastive learning methods address the question using two major components: a method for quantifying the similarity (or dissimilarity) of pairs of evidence items, and a method for determining the best separation of within-source or between-source comparisons. Contrastive learning methods are particularly useful when the data derived from the evidence is high-dimensional or complex. In this case, the contrastive learning algorithms take advantage of high-performing artificial intelligence and machine learning tools to avoid specifying complicated probability models for the usual forensic statistics approaches. In this presentation, a contrastive learning algorithm framework is developed for complex evidence and applied to data from aluminum powder particles recovered from two pre-blast improvised explosive devices.

machine learning

explosives

likelihood ratio

scores

ROC curve

density estimation 

Section on Statistics in Defense and National Security