Unique fingerprints in 3D printing may foil adversaries

3D printing is a straightforward technique to create customized instruments, substitute items and different useful objects, however it’s also getting used to create untraceable firearms, similar to ghost weapons, just like the one implicated within the late 2024 killing of UnitedHealthcare CEO Brian Thompson.

Netanel Raviv, assistant professor of laptop science & engineering within the McKelvey Faculty of Engineering at Washington College in St. Louis, led a workforce from the departments of Laptop Science & Engineering and Biomedical Engineering that has developed a technique to create an embedded fingerprint in 3D-printed components that might stand up to the merchandise being damaged, permitting authorities to realize info for forensic investigation, such because the identification of the printer or the one who owns it and the time and place of printing.

The analysis will likely be introduced on the USENIX Security Symposium Aug. 13–15, 2025, in Seattle. The primary authors of the paper are Canran Wang and Jinweng Wang, who earned doctorates in laptop science in 2024 and 2025, respectively. The analysis is published on the arXiv preprint server.

Fingerprinting in 3D printing embeds distinctive, traceable knowledge, similar to timestamps, geolocations and printer identification, into every merchandise and permits objects to be traced to the creator. Whereas there are a number of methods to create fingerprints, none have checked out how these fingerprints might stand as much as somebody who tampers with or breaks the merchandise into items.

Raviv’s workforce developed mathematical methods to embed info into 3D printed objects in a strong manner and matched them with safety mechanisms to implement 3D-printers to embed these codes within the objects they print. The workforce’s framework, Safe Info Embedding and Extraction, or SIDE, makes use of break-resilient, loss-tolerant embedding methods that arise in opposition to adversaries and acts as a safety mechanism.

The method is built on research Raviv and his scholar introduced in July 2024 on the IEEE Worldwide Symposium on Info Principle. That analysis centered on making a mathematical framework for an encoder that might get better authentic info bits from fragments of damaged 3D printed objects ensuing from adversarial tampering.

“This work opens up new venues for shielding the general public from the dangerous features of 3D printing through a mixture of mathematical contributions and new safety mechanisms,” Raviv mentioned. “Whereas SIDE has limitations in defending in opposition to resourceful attackers with sturdy experience in 3D printing, it considerably raises the extent of sophistication, prior information and experience required from the adversary to stay undetected after committing the crime.”