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We propose an end-to-end solution, from watermark feature generation tometric design, for effectively demoting watermarked images surfed by a realworld image search engine. We use a few fundamental techniques to obtaineffective watermark features of images in the image search index, and utilizethe 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 labelsindicating whether the image contains visible watermark. We train a few deepconvolutional neural networks to extract watermark information from the rawimages. The deep CNN classifiers we trained can achieve high accuracy on thewatermark test data set. We also analyze the images based on their domains toget watermark information from a domain-based watermark classifier. We design anew novel hybrid metric which includes the relevance, image attractiveness andwatermark information all together. We demonstrate that using these watermarksignals together with the new metric in image search ranker can significantlydemote the watermarked images during the online image ranking.

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