Secure Information Embedding in Forensic 3D Fingerprinting

TL;DR

This paper introduces SIDE, a novel 3D printing fingerprinting framework that securely embeds and extracts identifiable information, enhancing forensic tracking of 3D-printed objects against adversarial attacks.

Contribution

The paper presents a new coding-theoretic approach combined with Trusted Execution Environments for resilient and secure 3D print fingerprinting, addressing adversarial challenges.

Findings

SIDE is break-resilient and loss-tolerant

Effective in recovering fingerprints from fragmented prints

Secure embedding process protected by TEE

Abstract

Printer fingerprinting techniques have long played a critical role in forensic applications, including the tracking of counterfeiters and the safeguarding of confidential information. The rise of 3D printing technology introduces significant risks to public safety, enabling individuals with internet access and consumer-grade 3D printers to produce untraceable firearms, counterfeit products, and more. This growing threat calls for a better mechanism to track the production of 3D-printed parts. Inspired by the success of fingerprinting on traditional 2D printers, we introduce SIDE (\textbf{S}ecure \textbf{I}nformation Embe\textbf{D}ding and \textbf{E}xtraction), a novel fingerprinting framework tailored for 3D printing. SIDE addresses the adversarial challenges of 3D print forensics by offering both secure information embedding and extraction. First, through novel coding-theoretic techniques, SIDE is both~\emph{break-resilient} and~\emph{loss-tolerant}, enabling fingerprint recovery even if the adversary breaks the print into fragments and conceals a portion of them. Second, SIDE further leverages Trusted Execution Environments (TEE) to secure the fingerprint embedding process.