# An End-to-End Solution for Effectively Demoting Watermarked Images in Image Search

## TL;DR

This paper presents an end-to-end approach combining watermark feature extraction and a hybrid ranking metric to effectively demote watermarked images in search results, improving image search quality.

## Contribution

It introduces a novel hybrid metric incorporating watermark signals and demonstrates its effectiveness in demoting watermarked images in search rankings.

## Key findings

- Deep CNNs achieve high accuracy in watermark detection.
- Domain-based watermark classification enhances detection.
- The hybrid metric significantly reduces watermarked images in search results.

## Abstract

We propose an end-to-end solution, from watermark feature generation to metric design, for effectively demoting watermarked images surfed by a real world image search engine. We use a few fundamental techniques to obtain effective watermark features of images in the image search index, and utilize the signals in a commercial search engine to improve the image search quality. We collect a diverse and large set (about 1M) of images with human labels indicating whether the image contains visible watermark. We train a few deep convolutional neural networks to extract watermark information from the raw images. The deep CNN classifiers we trained can achieve high accuracy on the watermark test data set. We also analyze the images based on their domains to get watermark information from a domain-based watermark classifier. We design a new novel hybrid metric which includes the relevance, image attractiveness and watermark information all together. We demonstrate that using these watermark signals together with the new metric in image search ranker can significantly demote the watermarked images during the online image ranking.