SaTC: CORE: Small: Forensic Coding: Robust Information Embedding in 3D Printed Objects.

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3D printing brings about a revolution in consumption and distribution of goods, but poses a significant risk to public safety. Any individual with internet access and a commodity printer can now produce untraceable firearms (ghost guns), keys, and dangerous counterfeit products. To combat these threats, it is important to embed identifying information inside the object, so that the perpetrator could be identified if the object is confiscated or left at a crime scene. Embedding information in a 3D printed object is a relatively new area, and various engineering solutions have been proposed as of late. Yet, for security applications, information embedding techniques must be resilient to adversarial tampering, so that the identifying information could be extracted even if the object was intentionally damaged. To this end, this project focuses on developing robust methods for embedding information inside 3D printed objects using the theory of error correcting codes.Correcting adversarially induced errors is a well-studied topic in coding theory, and many good codes exist. However, since adversaries can physically tamper with a 3D object, 3D printed information is prone to unique noise patterns that are not addressed by existing codes. In the proposed work, these patterns will be analyzed and abstracted mathematically, and new codes will be developed to combat any limited adversary. The project is expected to develop practical mechanisms for worst-case robustness in 3D information embedding, as well as new coding-theoretic concepts, and experimental work at the proof-of-concept level.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.