An End-to-End Solution for Effectively Demoting Watermarked Images in Image Search
This work addresses the issue of low-quality watermarked images in commercial image search engines, though it is incremental as it builds on existing techniques.
The paper tackled the problem of watermarked images appearing in image search results by developing an end-to-end solution that uses deep CNNs and a hybrid metric to demote such images, achieving significant demotion in online ranking.
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.