Open-Set Learning-Based Hologram Verification System Using Generative Adversarial Networks - Ay, Betul (Ieee-Inst Electrical Electronics Engineers Inc, 2022)
In this study, we address the hologram authenticity challenge by introducing a novel deep-learning based end-to-end hologram verification system. The system ultimately makes the decision whether the hologram image captured from a mobile application is fake or not by employing a robust classifier. We built the system by training three major deep networks; generative networks, convolutional networks and region-based convolutional networks. One major challenge in this study was the lack of negative class samples or so-called fake holograms. To the best of our knowledge there are no publicly available fake hologram datasets and it is not clear how the attackers imitate the real holograms. Therefore, the negative class in the practical hologram classification task is actually unknown class, as it is unknown how to imitate holograms by attackers. We hereby consider the problem of hologram classification as in a similar logic to open-set recognition. To make hologram classifier more sensitive to forgery, we generate synthetic images using generative adversarial networks (GANs) to represent negative class. We conduct extensive and comparative experiments on the closed-set and open-set using the-state-of-the-art backbone convolutional neural networks (CNNs). The proposed system gives an impressive accuracy 97.5% and 79% for closed-set and open-set samples, respectively. The reported results show the strong generalization performance of the system for unknown samples.
Keywords: Generative adversarial networks; Generators; Training; Deep learning; Security; Task analysis; Convolutional neural networks; Deep learning; generative adversarial networks; convolutional neural networks; image classification; computer vision