Two-Stage Visible Watermark Removal Architecture Based On Deep Learning

With the rapid development of the Internet, watermarks are widely used in images to protect copyright. This implies that the robustness of watermark is very important. In recent years, there have been some studies to evaluate watermark performance by removing the watermark. Among them, some methods need to mark the watermark position in advance, and some require multiple images with the same watermark. Moreover, when the colour of thewatermark is similar to that of the background, the existing methods can hardly remove the watermark from the watermarked image. In the proposed work, the authors presented a watermark removal structure consisting of watermark extraction and image inpainting to address the aforementioned issues. In particular, the extraction network is used to extract the watermark in the watermarked image, and the inpainting network is used to inpainting image for a better watermark removal image, respectively. Finally, the authors train and test the developed network architecture by constructing two data sets, i.e. white watermarked image data set (WW-data set) and colour watermarked image data set (CW-data set). The proposed method not only has better performance on the WW-data set than the current latest methods (on the CW-data set, other methods have almost failed) but also effectively removes the watermarks.