  With the growing popularity of the Internet, digital images are used and
transferred more frequently. Although this phenomenon facilitates easy access
to information, it also creates security concerns and violates intellectual
property rights by allowing illegal use, copying, and digital content theft.
Using watermarks in digital images is one of the most common ways to maintain
security. Watermarking is proving and declaring ownership of an image by adding
a digital watermark to the original image. Watermarks can be either text or an
image placed overtly or covertly in an image and are expected to be challenging
to remove. This paper proposes a combination of convolutional neural networks
(CNNs) and wavelet transforms to obtain a watermarking network for embedding
and extracting watermarks. The network is independent of the host image
resolution, can accept all kinds of watermarks, and has only 11 layers while
keeping performance. Performance is measured by two terms; the similarity
between the extracted watermark and the original one and the similarity between
the host image and the watermarked one.
