  Since real-time contents can be captured and downloaded very easily,
copyright infringement has become a serious problem. In order to reduce the
loss caused by copyright infringement, copyright owners insert a watermark in
the content to protect the copyright using illegal distribution route tracking
and copyright authentication. However, whereas many existing watermarking
techniques are robust to signal distortion such as compression, they are
vulnerable to geometric distortion that causes synchronization errors. In
particular, capturing real-time content in Internet browsers and smartphone
applications is problematic because geometric distortion such as scaling and
translation frequently occurs. In this paper, we propose a convolutional neural
network-based template architecture that compensates for the disadvantages of
existing watermarking techniques that are vulnerable to geometric distortion.
The proposed template consists of a template generation network, a template
extraction network, and a template matching network. The template generation
network generates a template in the form of noise and the template is inserted
into certain pre-defined spatial locations of the image. The extraction network
detects spatial locations where the template is inserted in the image. Finally,
the template matching network estimates the parameters of the geometric
distortion by comparing the shape of spatial locations where the template was
inserted with the locations where the template was detected. It is possible to
recover an image in its original geometrical form using the estimated
parameters, and as a result, watermarks applied using existing watermarking
techniques that are vulnerable to geometric distortion can be decoded normally.
