In a scenario where the use of video applications is globally increasing, video copy detection is becoming an indispensable task for many applications such as content management, searching, retrieval, authentication, integrity verification, monitoring, tracking, video management, monetization, and watermarking support. A significant challenge for current copy detection methods is the increasing number of video transformations produced intentionally or by the signal processing and video transmission. Thus, robust and discriminant copy detection methods are required to face those changes. A practical approach widely used for video copy detection is a passive or content-based fingerprinting method. In the past decade, although many robust fingerprinting methods have been proposed, those are not sufficiently discriminant and efficient for real scenarios. Therefore, an additional challenge is to achieve copy detection with high precision in realtime processing, especially for applications that do not tolerate false alarms, e.g. in video streaming transmissions. That is, faster-fingerprinting methods are necessary to process an immense amount of data in applications in real-time and at the same time robust against common and severe video transformations.