One Video to Steal Them All: 3D-Printing IP Theft through Optical Side-Channels

TL;DR

This paper demonstrates that adversaries can reverse engineer 3D printing instructions from video recordings, enabling intellectual property theft and counterfeiting, with high accuracy and fewer instructions than existing methods.

Contribution

The paper introduces a novel video-based reverse engineering approach for 3D printing instructions, including an equivalence checker that is invariant to rotation and translation.

Findings

Achieved 90.87% accuracy in reverse engineering instructions.

Generated 30.20% fewer instructions than existing methods.

Successfully created counterfeit objects from video recordings.

Abstract

The 3D printing industry is rapidly growing and increasingly adopted across various sectors including manufacturing, healthcare, and defense. However, the operational setup often involves hazardous environments, necessitating remote monitoring through cameras and other sensors, which opens the door to cyber-based attacks. In this paper, we show that an adversary with access to video recordings of the 3D printing process can reverse engineer the underlying 3D print instructions. Our model tracks the printer nozzle movements during the printing process and maps the corresponding trajectory into G-code instructions. Further, it identifies the correct parameters such as feed rate and extrusion rate, enabling successful intellectual property theft. To validate this, we design an equivalence checker that quantitatively compares two sets of 3D print instructions, evaluating their similarity in producing objects alike in shape, external appearance, and internal structure. Unlike simple distance-based metrics such as normalized mean square error, our equivalence checker is both rotationally and translationally invariant, accounting for shifts in the base position of the reverse engineered instructions caused by different camera positions. Our model achieves an average accuracy of 90.87 percent and generates 30.20 percent fewer instructions compared to existing methods, which often produce faulty or inaccurate prints. Finally, we demonstrate a fully functional counterfeit object generated by reverse engineering 3D print instructions from video.