Hiding My Real Self! Protecting Intellectual Property in Additive Manufacturing Systems Against Optical Side-Channel Attacks

We propose an optical side-channel attack to recover intellectual property in Additive Manufacturing (AM) systems. Specifically, we use a deep neural network to estimate the coordinates of the printhead as a function of time by analyzing the video of the printer frame by frame. We found that the deep neural network can successfully recover the path for an arbitrary printing process. By using data augmentation, the neural network can tolerate a certain level of variation in the position and angle of the camera as well as the lighting conditions. The neural network can intelligently perform interpolation and accurately recover the coordinates of an image that is not seen in the training dataset.