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Such tools must also be non-invertible (computationally hard to reverse engineer), and adaptable to different sub-applications and hardware setups. Convolutional Neural Networks (CNNs), which learn spatial characteristics, are commonly employed for such pattern recognition tasks. In this paper, we demonstrate the uniqueness of particle-filled polymeric composites for electronics assembly authentication. These composites, when embedded within or on a substrate, serve as physical taggants that reveal tampering or replacement. Physically valid synthetic microstructures mimicking particle-filled polymers are generated using a modified particle-packing algorithm and used to train neural networks. Our results show that networks, which train solely on classification, struggle with the open-set nature of the counterfeit data, often producing similar probability scores and are hard to classify. However, when reduced order representations of images are assumed gaussian and evaluated, the probability of authenticity can be precisely learned and used for robust classification, achieving accurate separation of true and counterfeit data. Networks which directly learn the probabilistic distribution of the true data and those that plot hyper sphere dimensionality reduction, similarly achieve near 100% accuracy on synthetic data, though their precision drops for counterfeit microstructures with closely related properties. These findings suggest that while classification based learning alone is not adequate, their trained latent representations provide reliable solutions for counterfeit detection in microstructural fingerprinting.<\/jats:p>","DOI":"10.1007\/s41635-026-00181-5","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T07:57:00Z","timestamp":1780559820000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Particulate Composites Aided Counterfeit Electronic Assembly Detection Using Generative Network Models"],"prefix":"10.1007","volume":"10","author":[{"given":"Tejas Ravindra","family":"Kulkarni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikhilesh","family":"Chawla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ganesh","family":"Subbarayan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,4]]},"reference":[{"key":"181_CR1","unstructured":"Khan Saif\u00a0MAM, Peterson D (2021) The semiconductor supply chain: Assessing national competitiveness, center for security and emerging technology 8. https:\/\/cset.georgetown.edu\/publication\/the-semiconductor-supply-chain\/. 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G.S. and N.C. provided supervision, conceptual guidance throughout the study, and critical revisions of the manuscript. All authors reviewed and approved the final manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Author Contributions"}},{"value":"The synthetic particulate composites generated and analyzed in this study were produced using a modified drop fall shake (MDFS) particle packing algorithm implemented in MATLAB. The dataset comprises of 16 microstructure classes with variations in volume fraction, aspect ratio, clustering, and orientation. Their 2D projection images used for CNN and autoencoder training are available from the author upon request","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data Availability"}},{"value":"Not applicable. This study involved no human participants, no animal subjects, and no data requiring ethical approval.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}],"article-number":"10"}}