LRS-Net: invisible QR Code embedding, detection, and restoration

Abstract

QR code is a powerful tool to bridge the offline and online worlds. It has been widely used because it can store a large amount of information in a small space. However, the black-and-white style of QR codes is not attractive to the human eyes when embedded in videos, which greatly affects the viewing experience. Invisible QR code has proposed based on temporal psycho-visual modulation (TPVM) to embed invisible hyperlinks in shopping websites, copyright watermarks in movies, etc. However, existing embedding and detection methods are not robust enough. In this paper, we adopt a novel embedding method to greatly improve the visual quality of the embedded video. Furthermore, we build a new dataset of invisible QR codes named 'IQRCodes' to train deep neural networks. At last, we propose localization, refinement, and segmentation neural netowrks (LRS-Net) to efficiently detect and restore invisible QR codes that are captured by mobile phones.