A Robust and Fast Video Copy Detection System Using Content-Based Fingerprinting

Abstract

A video copy detection system that is based on content fingerprinting and can be used for video indexing and copyright applications is proposed. The system relies on a fingerprint extraction algorithm followed by a fast approximate search algorithm. The fingerprint extraction algorithm extracts compact content-based signatures from special images constructed from the video. Each such image represents a short segment of the video and contains temporal as well as spatial information about the video segment. These images are denoted by temporally informative representative images. To find whether a query video (or a part of it) is copied from a video in a video database, the fingerprints of all the videos in the database are extracted and stored in advance. The search algorithm searches the stored fingerprints to find close enough matches for the fingerprints of the query video. The proposed fast approximate search algorithm facilitates the online application of the system to a large video database of tens of millions of fingerprints, so that a match (if it exists) is found in a few seconds. The proposed system is tested on a database of 200 videos in the presence of different types of distortions such as noise, changes in brightness/contrast, frame loss, shift, rotation, and time shift. It yields a high average true positive rate of 97.6% and a low average false positive rate of 1.0%. These results emphasize the robustness and discrimination properties of the proposed copy detection system. As security of a fingerprinting system is important for certain applications such as copyright protections, a secure version of the system is also presented.