As the bandwidth accessible to average users has increased, audiovisual material has become the fastest growing datatype on the Internet. The impressive growth of the social Web, where users can exchange user-generated content, contributes to the overwhelming number of multimedia files available. Among these huge volumes of data, a large numbers of near duplicates and copies exist. File copies are easy to detect using hashes. However, near duplicates are based on the same original content but have been edited and postprocessed, resulting in different files. Another type of near duplicate includes footage of the same event or scene. Detecting near duplicates poses a challenge for multimedia content analysis, especially when speed, scale, and copied fragment length are pushed to operational levels. Near duplicates carry both informative and redundant signals, for example, providing rich visual clues for indexing and summarizing videos from different sources. Still, the excessive amount of near duplicates streamed over Internet demands scalable techniques for copyright infringement detection, advertisement tracking, and content monitoring for forensic applications. As a result, there is strong interest from industry, academia, and governmental agencies in Web-scale search, elimination, detection, and use of near duplicates for various multimedia applications. This special issue presents some of the most recent advances in the research on Web-scale near-duplicate search and explores the potential for bringing this research a substantial step further. It contains high-quality contributions addressing various aspects of the Webscale near-duplicate search problem in a numberof relevant domains.