Secure Information Embedding in Forensic 3D Fingerprinting

TL;DR

Secure Information Embedding in Forensic 3D Fingerprinting presents SIDE, a framework that secures data embedded in 3D printed parts against adversarial fragmentation and tampering. It combines α-break-resilient codes for robust, loss-tolerant recovery with a Trusted Execution Environment protected embedding pipeline and progressive slicing to fit secure hardware constraints. A working prototype on a Creality Ender 3 with a Raspberry Pi and OP-TEE demonstrates reliable fingerprint recovery under fragmentation, practical code rates, and acceptable overhead with minimal impact on print quality. The work enables traceability in distributed 3D printing while raising the bar against forging and hardware-tampering attacks in forensic contexts.

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.

Figures (13)

• Figure 1: (a) Renderings of a Glock 19 frame design. (b) Fingerprinting fails when the frame is broken, with portions missing.
• Figure 2: Law Enforcement Agencies Fail Fingerprinting
• Figure 3: Fragments of a transmission shaft. Breaks in (a) cross multiple layers, and the assembly of fragments may be inferred from their overlapping bits. Breaks in (b) are perpendicular to the $z$ direction, and the correct assembly (i.e., order) cannot be inferred from the fragments themselves.
• Figure 4: Demonstration of embedding $01101001$ with parameters $x=0.08$ and $(y,\epsilon)=(0.12,0.02)$. In the normal settings, each $0$ is represented by two layers of $0.08$ and $0.16$ millimeters, and each $1$ is represented by one layers of $0.24$ millimeters. In the stealthy settings, each $0$ is represented by two layers of $0.10$ and $0.14$ millimeters, and each $1$ is represented by two layers of $0.12$ millimeters. In either case, the length required for embedding one bit is $0.24$ mm.
• Figure 5: Procedure of bit extraction.

Theorems & Definitions (9)

• Remark 1
• Theorem 1
• Lemma 1
• Theorem 2
• proof
• Lemma 2
• proof
• Lemma 3
• proof