We propose a method for ensuring traceability of metal goods in an efficient and secure manner that leverages data obtained from micrographs of a part’s surface that is instance specific (i.e., different for another instance of that same part). All stakeholders in modern supply chains face a growing need to ensure quality and trust in the goods they produce. Complex supply chains open many opportunities for counterfeiters, saboteurs, or other attackers to infiltrate supply networks, and existing methods for preventing such attacks can be costly, invasive, and ineffective. The proposed method extracts discriminatory-yet-robust intrinsic strings using features extracted from the two-point autocorrelation data of surface microstructures, as well as from local volume fraction data. By using a synthetic dataset of three-phase micrographs similar to those obtained from metal alloy systems using low-cost optical microscopy techniques, we discuss tailoring the method with respect to cost and security and discuss the performance of the method in the context of anticounterfeiting and how similar methods may be evaluated for performance. Cryptographic extensions of this methodology are also discussed.