Everyday and obtains a series of scale-invariant feature

Everyday very huge
amount of data is embedded on digital media or distributed over the internet.
The data is so distributed that it can be replicated easily without any error.
Even with encryption technique, when distributed it can be decrypted and copied.
One way to avoid this is to make use of digital watermarking technology. It is
a technology that embeds watermark into images, audios, videos and other
multimedia data with the help of an algorithm. Watermark is information that
can be later extracted for authentication and identification purposes. The
problem of illegal manipulation and distribution of digital video is becoming a
big issue. This issue is solved by embedding copyright information into bit
streams of any video. In the existing system, DCT based Watermarking, an image watermarking technique is used to
add a code to digital images. This method operates in frequency domain
embedding a pseudo-random sequence of real numbers in a selected set of DCT
coefficient. It is done is such a way to ensure non-erasability of the image
watermarking. While it ensures non-erasability, it does introduce Gaussian
noise during the watermarking process, the contrast and brightness of the
hidden image will be affected due to the information watermarking and the overall image after
watermarking consumes a lot of memory. In this paper, PCA (Principal Component
Analysis) based Framelet Transform is combined with local digital
watermarking algorithm and digital watermarking algorithm based SVD (Singular Value Decomposition) is
proposed. It describes the generation process of the
PCA FT SVD algorithm in detail and obtains a series of scale-invariant feature
points. A large amount of candidate feature points are selected to obtain the
neighborhood which can be used to embed the watermark. The advantages of the
proposed system are robustness against watermarking attacks, imperceptibility,
capacity and security.

Video Watermarking, Principal Component Analysis, Framelet Transform, Singular
Value Decomposition, Robustness, Copyright Protection

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