# Convolutional Neural Network Architecture for Recovering Watermark Synchronization
## 6 Discussion and Conclusions  
Conventional template-based watermarking techniques usually use whole image-unit transform rather than block-unit transform. However, the image-unit template has a problem in that the template cannot be inserted independently with the watermark containing copyright information unless they use the same transform domain. Therefore, there is a limitation that the template and watermark signal can interfere with each other. Also, it is difficult to design a multi-bit watermarking system in comparison with the block-unit method. On the other hand, since the proposed template is a block-unit method and can be separated spatially, it does not cause interference with the watermark. Moreover, because it is a learning-based method, it has the advantage of being able to respond quickly to new types of distortion. Due to these advantages, the proposed template can be applied not only to conventional watermarking methods but also to newly proposed watermarking methods for various purposes. Figure 10: Average BER for RST attack with DIBR rendering. (**a**) rotation, (**b**) scaling, (**c**) translation. For example, the proposed template can be applied to a new form of watermarks such as the recently proposed depth-image-based rendering (DIBR) watermarking technique. DIBR is a rendering method to give a stereoscopic effect to images [40; 41], but it cannot be protected by conventional watermarks and templates because it causes horizontal non-linear distortion. To cope with this, various DIBR watermarking techniques have been proposed [42; 43; 31; 44], but they show weaknesses in geometric distortion since there have been few studies on the topic. The proposed learning-based and block-based template can easily solve the problem of geometric distortion for the DIBR watermark. However, the proposed template-based watermarking system also has an inherent problem. Instead of acquiring robustness, there is an increase in computational cost for matching the embedded template. For example, compared to the case of applying only the curvelet transform, the watermark detection time increases by about 0.6 s on average when the proposed template is used. In the case of watermark, about 0.6 s of CPU time comes out when decoding a 512 \(\times\) 512 video (based on Intel 6700 K). The proposed method uses only half of the image area, resulting in around 0.3 s of CPU time. In the case of the template, it takes around 0.25 s of GPU time to decode (based on GTX 1080). This can be a weakness when processing large-scale image data (e.g., web-scale image database) and improvement in template processing time will be needed in future studies. In terms of cost-efficiency, model simplification of the deep neural network [45; 46] should be helpful to improve the performance. Recently, Webcomic companies and content providers of IPTV have started actively using these techniques to track and punish illegal distribution. Watermarking techniques and templates based on CNN are still in the early stage for these applications. Further research is needed to improve watermark performance such as masking techniques that will increase invisibility and security enhancement. As the market for real-time content services such as web-comics (also called webtoons) and video streaming services such as YouTube has grown very rapidly, the demands of the watermarking framework have to be not only user friendly but also requires computing efficiency. There are some potential implementation of the proposed method based on the other machine learning techniques including graphical models, and different CNN architectures such as dilated convolution [47]. Also, we will extend the scope of our research into video content watermarking using CNN. Conceptualization, W.-H.K. ; Methodology, W.-H.K. ; Software, W.-H.K. ; Writing-Original Draft Preparation, W.-H.K. ; Writing-Review and Editing, J.K., S.-M.M. and J.-U.H. ; Validation, J.K.; Software, S.-M.M. ; Conceptualization, Supervision and Funding Acquisition, J.-U.H.