Yes that’s probably how you would do this. Get a bunch of data of gcode of 3d printed gun parts and not-gun parts, for different slicers and printers. Then train some transformer as a classifier. Based on how good object recognition is, i would say its possible that you would get reasonably good accuracy and precision. And because you are scanning for code the architecture will likely be similar to an llm.
Then five minutes later, someone figures out how to make a 3d printable gun that bypasses the gun detector on the 3d printer. It’s not like you’re printing a whole gun; you’re printing parts, most of which look nothing like a gun. How hard would it be to design an algorithm that takes a gun part cad file and then adds a bunch of extraneous pieces to it that can be easily removed? Just keep adding extra crap until the system no longer detects it as a gun part.
Yes that would probably work. There could be some essential features of weapon parts that an algorithm might still be able to learn, and a printer could also keep track of previously printed parts for the classification. I think its unlikely that there are essential features of gun parts that are specific to gun parts so there would probably be a lot of false positives.
Yeah I’m thinking of an automated version of greebling. Except you design the extraneous bits so that they’re only attached to the intended print like print supports - something in a non-critical location, easily torn away.
Yes that’s probably how you would do this. Get a bunch of data of gcode of 3d printed gun parts and not-gun parts, for different slicers and printers. Then train some transformer as a classifier. Based on how good object recognition is, i would say its possible that you would get reasonably good accuracy and precision. And because you are scanning for code the architecture will likely be similar to an llm.
Then five minutes later, someone figures out how to make a 3d printable gun that bypasses the gun detector on the 3d printer. It’s not like you’re printing a whole gun; you’re printing parts, most of which look nothing like a gun. How hard would it be to design an algorithm that takes a gun part cad file and then adds a bunch of extraneous pieces to it that can be easily removed? Just keep adding extra crap until the system no longer detects it as a gun part.
Yes that would probably work. There could be some essential features of weapon parts that an algorithm might still be able to learn, and a printer could also keep track of previously printed parts for the classification. I think its unlikely that there are essential features of gun parts that are specific to gun parts so there would probably be a lot of false positives.
Yeah I’m thinking of an automated version of greebling. Except you design the extraneous bits so that they’re only attached to the intended print like print supports - something in a non-critical location, easily torn away.
I think a problem might be that the “easily broken away” might be a feature that an algorithm might be able to learn.