# Convolutional Neural Network Architecture for Recovering Watermark Synchronization
## 1 Introduction  
Digital content has always been subject to copyright infringement due to its ease of duplication. Recently, 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 problem of copyright infringement is increasing. These services, unlike traditional paid services, offer free content and earn revenue by advertising to users. As a result, these services are more vulnerable to illegal copying because they are easier to access than services that are provided for a fee. In addition, these services are provided on Internet browsers or as smartphone applications, making it easier to replicate the content with the Internet browsers' or applications' downloading or capturing functions.  
Watermarking techniques have emerged to reduce the loss caused by piracy. These techniques minimize loss through the insertion of invisible information in the content to enable tracking of illegal distribution routes and copyright authentication. As mentioned above, in real-time content provided by Internet browsers and applications, geometric distortion occurs very frequently due to capturing. Figure 1 shows an illegal distribution scenario that occurs in a real-time content service and a watermark extraction process suitable for such a scenario. The piracy process, such as the capturing shown in this figure, makes it difficult to decode the watermark by breaking the synchronization of the watermark. Correction of the geometric distortion must be performed to extract the watermarkaccurately. Therefore, there is a need for a method that can effectively recover the image after such geometric distortion.  
In this paper, we propose a convolutional neural network (CNN)-based template that can effectively correct geometric distortion. The proposed method divides the image into blocks of a specific size, inserts bit information into half of the blocks using the conventional watermarking technique, and inserts a CNN-based template to correct the geometric distortion in the other half. By inserting these block-based templates, it is possible to compensate for the disadvantages of geometric distortion while preserving the advantages of existing watermarking techniques except for the bit capacity.  
The contributions of the proposed method are as follows:  
1. A learning-based template network that restores geometric distortion. Through learning, a robust template against various attacks can be designed.
2. Block-unit template. This makes it easy to design a multi-bit watermarking system and does not interfere with the watermark signal because it can be inserted independently with the watermark.
3. Due to the above two characteristics, the template can be easily applied to other watermarking techniques. This complements the vulnerability to geometric distortion of new forms of watermarks such as the DIBR watermarking method as well as existing watermarks.  
The remainder of this paper is organized as follows. Section 2 summarizes related works of robust watermarking and template-based watermarking. Section 3 presents the main concept of the proposed method, and Section 4 discusses the proposed method. Section 5 presents experimental results and Section 6 concludes the paper.