Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : May 27, 2020 - May 30, 2020
In this paper, the author aims to embed arbitrary information in arbitrary images and restore them using convolutional neural networks. To achieve this goal, we propose two sets of CNN models for embedding and restoring information in images.Thanks to the error correction nature of QR code, embedded information is expected to be restored without errors. However, in the previous model, the proposed method could only be applied to blurred images. This time, the model has been improved so that the QR code can be embedded in a high-resolution image and restored.
