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.
just rotate the piece at different angles on the plate, which would change the positions of vertexes and generate an unrecognizable set of printer head instructions. No extra pieces necessary; and even if there were, there’s no need for them to be printed attached to the main part.
Isleepinahammocks idea would probably work. But rotation and translation would not. Thats something you can easily take care of in your training data, by reusing the same training data in multiple random positions and random angles.
I bet they end up using a fucking llm
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.
just rotate the piece at different angles on the plate, which would change the positions of vertexes and generate an unrecognizable set of printer head instructions. No extra pieces necessary; and even if there were, there’s no need for them to be printed attached to the main part.
Isleepinahammocks idea would probably work. But rotation and translation would not. Thats something you can easily take care of in your training data, by reusing the same training data in multiple random positions and random angles.
are you familiar with 3d printing? The print head’s instructions will be completely different if you rotate the model. Unrecognizably different.