G-ID: Identifying 3D Prints Using Slicing Parameters
Mustafa Doga Dogan, Faraz Faruqi, Andrew Day Churchill, Kenneth Friedman, Leon Cheng, Sriram Subramanian, Stefanie Mueller
Abstract
We present G-ID, a method that utilizes the subtle patterns left by the 3D printing process to distinguish and identify objects that otherwise look similar to the human eye. The key idea is to mark different instances of a 3D model by varying slicing parameters that do not change the model geometry but can be detected as machine-readable differences in the print. As a result, G-ID does not add anything to the object but exploits the patterns appearing as a by-product of slicing, an essential step of the 3D printing pipeline.
Topics & Concepts
SlicingComputer sciencePipeline (software)ExploitKey (lock)Process (computing)Object (grammar)3D printingProduct (mathematics)Artificial intelligenceSolid modelingComputer graphics (images)Computer visionEngineering drawingProgramming languageEngineeringMathematicsGeometryMechanical engineeringComputer securityPhysical Unclonable Functions (PUFs) and Hardware SecurityIndustrial Vision Systems and Defect DetectionVisual Attention and Saliency Detection