MaGNIFIES: Manageable GAN Image Augmentation Framework for Inspection of Electronic Systems - Ghosh, Pallabi; Lee, Gijung; Zhu, Mengdi; Dizon-Paradis, Olivia P.; Botero, Ulbert J.; Woodard, Damon L.; Forte, Domenic (Journal of hardware and systems security, 2024)
Electronic counterfeiting is a long-lasting problem that continues to cost original manufacturers billions, fund organized crime, and jeopardize national security and mission-critical infrastructures. Manual inspection is a popular and standardized way to detect counterfeit electronic components, but it is time-consuming and requires subject matter experts for classification. State-of-the-art machine learning, deep learning, and computer vision-based physical inspection methods are promising to alleviate these issues. However, the main bottleneck for doing so is a lack of high-quality, publicly available counterfeit image data for training. Producing such datasets is also time-consuming and often requires expensive equipment. In addition, most test labs are not allowed to freely publish images taken from their customer’s chips. One solution to this data shortage bottleneck can be addressed by augmenting synthetic data. In this paper, (i) data multiplication using Progressive GAN, StyleGAN, and classical methods in counterfeit data domain is explored; (ii) a novel framework, named MaGNIFIES, is proposed; and (iii) an efficient Convolutional Neural Network architecture is proposed, which can detect defective parts by training only on the synthetic dataset generated using (i) or (ii). For proof of concept, we have used low-quality images of resistors and capacitors with and without scratch defects as counterfeit and golden components respectively. We have also illustrated how our approach using MaGNIFIES addresses the shortcomings of the existing augmentation methods. Separate data augmentation detection models are trained with each type of augmented data generated using MaGNIFIES, as well as existing techniques, and tested on a test set of real data.