Single Fragment Forensic Coding from Discrepancy Theory
Junsheng Liu, Netanel Raviv

TL;DR
This paper introduces novel coding schemes for 3D printed objects that enable the recovery of embedded data from partial fragments, enhancing forensic traceability and security in additive manufacturing.
Contribution
It develops new encoding methods for 2D and 3D data embedding in 3D printing, allowing decoding from large fragments and correcting errors, using discrepancy theory concepts.
Findings
Codes operate at non-vanishing rates.
Decoding from any large rectangular or cuboid fragment is possible.
Error correction capabilities are integrated into the codes.
Abstract
Three-dimensional (3D) printing's accessibility enables rapid manufacturing but also poses security risks, such as the unauthorized production of untraceable firearms and prohibited items. To ensure traceability and accountability, embedding unique identifiers within printed objects is essential, in order to assist forensic investigation of illicit use. This paper models data embedding in 3D printing using principles from error-correcting codes, aiming to recover embedded information from partial or altered fragments of the object. Previous works embedded one-dimensional data (i.e., a vector) inside the object, and required almost all fragments of the object for successful decoding. In this work, we study a problem setting in which only one sufficiently large fragment of the object is available for decoding. We first show that for one-dimensional embedded information the problem can be…
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