  In this work we propose a novel technique to quantify how distracting
watermarks are on an image. We begin with watermark detection using a two-tower
CNN model composed of a binary classification task and a semantic segmentation
prediction. With this model, we demonstrate significant improvement in image
precision while maintaining per-pixel accuracy, especially for our real-world
dataset with sparse positive examples. We fit a nonlinear function to represent
detected watermarks by a single score correlated with human perception based on
their size, location, and visual obstructiveness. Finally, we validate our
method in an image ranking setup, which is the main application of our
watermark scoring algorithm.
