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USENIX Security2025顶会

Secure Information Embedding in Forensic 3D Fingerprinting

Canran Wang, Jinwen Wang, Mi Zhou, Vinh Pham, Senyue Hao, Chao Zhou, Ning Zhang, Netanel Raviv

2025年份

摘要

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 (Secure Information EmbeDding and Extraction), 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 break-resilient and 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.

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