Abstract
ABSTRACTDigital video is vulnerable to accidental or malicious destruction in storage, transmission, processing, which damages the legitimate rights and interests of the product owners. Digital video watermarking technology is a mean to protect intellectual property rights. We analyze the basic principle of the 3D-Harris algorithm and Gabor filter method. By considering the temporal causality of the video, the common symmetrical filter is discarded. We design a causal filter which conforms to video characteristics and proposes an asymmetric causal filter in space–time domain. On this basis, an improved video watermarking algorithm is proposed, combining space–time feature points and DCT domain. The experimental results show that proposed algorithm can not only ensure high invisibility, and can effectively resist various attacks in time domain and space domain.
Highlights
Video data integrates content about information in the environment and is widely applied in many visual tasks including surveillance, content authentication, service tracing, etc
We design a causal filter which conforms to video characteristics and proposes an asymmetric causal filter in space–time domain
Compared with Li’s and Bi’s scheme, our scheme is more robust against a variety of attacks, especially in the time domain. (Hongbo et al, 2011; Li et al, 2008)
Summary
Video data integrates content about information in the environment and is widely applied in many visual tasks including surveillance, content authentication, service tracing, etc. Video data can be represented by space–time interest points which have been used in video analysis, video interpretation, etc These space–time interest points reveal the intrinsic structure of video including the spatial and temporal dimension, which can be seen as a potential solution for temporal synchronization in video watermarking while preserving the advantage in spatial domain. The proposed method of space–time extraction can meet the following needs: (1) The algorithm is simple and can reduce the computational complexity as much as possible due to the huge amount of video data; (2) A temporal filter is introduced that detects enough points.
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