Near-infrared Imaging for Information Embedding and Extraction with Layered Structures

Non-invasive inspection and imaging techniques are used to acquire non-visible information embedded in samples. Typical applications include medical imaging, defect evaluation, and electronics testing. However, existing methods have specific limitations, including safety risks (e.g., X-ray), equipment costs (e.g., optical tomography), personnel training (e.g., ultrasonography), and material constraints (e.g., terahertz spectroscopy). Such constraints make these approaches impractical for everyday scenarios. In this article, we present a method that is low-cost and practical for non-invasive inspection in everyday settings. Our prototype incorporates a miniaturized near-infrared spectroscopy scanner driven by a computer-controlled 2D-plotter. Our work presents a method to optimize content embedding, as well as a wavelength selection algorithm to extract content without human supervision. We show that our method can successfully extract occluded text through a paper stack of up to 16 pages. In addition, we present a deep-learning-based image enhancement model that can further improve the image quality and simultaneously decompose overlapping content. Finally, we demonstrate how our method can be generalized to different inks and other layered materials beyond paper. Our approach enables a wide range of content embedding applications, including chipless information embedding, physical secret sharing, 3D print evaluations, and steganography.

Automated summary

This article introduces a low-cost and practical method for non-invasive inspection using a miniaturized near-infrared spectroscopy scanner and a computer-controlled 2D plotter. The method can successfully extract occluded text through a paper stack of up to 16 pages and improve image quality and decompose overlapping content using a deep-learning-based image enhancement model. It can also be applied to different inks and other layered materials. The approach enables various content embedding applications, including chipless information embedding, physical secret sharing, 3D print evaluations, and steganography.