Video copying detection method based on robust hash

Abstract

The invention discloses a video copying detection method based on robust hash. The advantages of spatial-temporal union information and a sequence characteristic in the aspect of reflecting video contents are used fully. The method comprises the following steps of: firstly, dividing a video frame into parts which are overlapped with one another in 50 percent and generating a time domain information representative image by using the video frame of each part; secondly, filling the time domain information representative images by using a hilbert curve to further calculate a gray scale three-meanvalue of each block on the hilbert curve, and generating a hash fingerprint by comparing the three-mean values; and finally, performing hash matching according to the obtained hash fingerprint and judging whether the video is a copied video. An experimental result shows that the robustness and the discrimination of an extracted characteristic are greatly improved and developed.

Description

technical field [0001] The invention relates to a robust hash-based video copy detection method, which belongs to the technical field of video and multimedia signal processing. Background technique [0002] With the continuous development of multimedia technology, a large amount of video information emerges, and people can conveniently use devices in their hands to shoot videos and edit digital videos. As a result, hundreds of videos are created every day on the Internet, and pirated videos are also multiplying, which greatly infringes the interests of copyright owners. In multimedia copyright protection, the copyright discriminator needs to quickly and effectively detect possible copies from a large amount of multimedia data and determine the ownership of the content. Currently in the network environment, content-based video copy detection has become a research hotspot in multimedia technology. [0003] In recent years, video copy detection technology has made great progr...

AI Technical Summary

Problems solved by technology

For example, color-based, motion-based, and edge-based methods have the following problems: color-based methods use color histograms, which cannot describe the spatial distribution of colors, so the description of features is incomplete during video copy detection, which affects the detection results.

There are also video data processing method and device patents mentioning that the grayscale range is obtained through the histogram, and the hash value is obtained through the number of pixels in different grayscale intervals, but the obvious disadvantage is that when the image is attacked, the color and texture features will change significantly, the robustness of the hash value cannot be guaranteed

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