Green-Noise Diffused Watermark Enhancing Both of the Amount of Embedded Information and Print Resistance by Machine Learning

Abstract

In the invisible digital watermarking method, image quality, resistance, and embedding information amount are in a trade-off relationship with each other, and it is usually difficult to satisfy all of them. The Green-Noise Diffused Watermarking Method has print/scan resistance and invisible by human eye. However, the amount of embedding information depends on the block size, and if the block size is reduced to increase the amount of embedded information, false judgement will increase. Therefore, we introduced machine learning to improve the identification accuracy. As a result, embedding 2048 bits in a 512 × 512 image with a 16 × 16 block size, we obtained correct answer rate of 99% for electronic data and 91% for printed/scanned images of 200ppi (94% for images of 175ppi or less), and achieved enhancing both robustness and embedding amount of information.