New Performance Evaluation Method for Data Embedding Techniques for Printed Images Using Mobile Devices Based on a GAN
Masahiro Yasuda, Soh Yoshida, Mitsuji Muneyasu · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · 2022
Methods that embed data into printed images and retrieve data from printed images captured using the camera of a mobile device have been proposed. Evaluating these methods requires printing and capturing actual embedded images, which is burdensome. In this paper, we propose a method for reducing the workload for evaluating the performance of data embedding algorithms by simulating the degradation caused by printing and capturing images using generative adversarial networks. The proposed method can represent various captured conditions. Experimental results demonstrate that the proposed method achieves the same accuracy as detecting embedded data under actual conditions.