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

arXiv: https://arxiv.org/abs/2506.21897 · HTML: https://mlsanity.com/paper/2506.21897/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2025.

## Built on by (most cited first, 2 of 2)

- [Immunizing 3D Gaussian Generative Models Against Unauthorized Fine-Tuning via Attribute-Space Traps](https://mlsanity.com/paper/2604.09688/) (2026)
- [Side-Channel Attacks Survive Noise Cancellation in 3D Printers](https://mlsanity.com/paper/2606.13952/) (2026)

## Abstract (arXiv)

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.