Haar wavelet based video watermarking algorithm for raw video

Mobile and Intelligent Sensing for Video Watermarking Using Spectral Centroid and Haar Wavelet Transformation on High Performace Computing

10.21203/rs.3.rs-395999/v1 ◽

2021 ◽

Author(s):

Venkatesh S ◽

Saravanakumar R ◽

SureshKumar M ◽

sivakumar B ◽

veeramakali T

Keyword(s):

Wavelet Transform ◽

Image Watermarking ◽

Digital Watermark ◽

Dft Method ◽

3D Models ◽

Haar Wavelet ◽

Video Watermarking ◽

Haar Wavelet Transform ◽

Spectral Centroid ◽

Digital Watermarks

Abstract Some technologies are technologically advanced to provide security from illegal copying. Two complementary methods are encryption and watermarking. Encryption safeguards the information throughout the communication from the sender to the receiver. The data might present a distorted image after receipt and subsequent decryption. Watermarking complements encryption through embedding data openly into the image. Therefore, the watermark continuously remains existing in the data. A digital watermark is a category of indication secretly entrenched in a noise-tolerant signal similar to audio or else image information. It is indeed applied to distinguish copyright possession of such signal. Computer-aided hiding of the given digitized information in a carrier is known as watermarking. Digital watermarks possibly will be employed to validate the authenticity or integrity of a carrier signal or to determine source uniqueness. It is evidently applied for determining copyright contraventions and aimed at banknote verification. Analogous to traditional watermarks, digital watermarks are unique only beneath certain conditions. Once a digital watermark varies a carrier in a manner that it turns out to be noticeable, formerly it is of no use. The media will be visible by traditional watermarks (similar to images or else video) but the signal might be pictures, video, audio, texts or 3D models in digital watermarking. A signal can transmit some different watermarks at the equivalent time. Image watermarking is achieved in this study using two methods known as Hidden Markov Tree–Contourlet Wavelet Transform (HMT-CWT) and Haar wavelet transform – Discrete Fourier transform (HWT-DFT). In the next HWT-DFT method, a video is given as an input and it is split into two halves (audio and image). The audio is de-watermarked through Spectral Centroid Wavelet Transform and enhanced by utilizing Firefly procedure. The images is handled through HWT in addition to DFT. Then the output watermarked images and audio combined together to form a watermarked video. The obtained video is de-watermarked to produce the original copy of the video. The process of getting back the original copy by removing the watermark from the video is called as de-watermarking.

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Haar Wavelet Transform and Multiobjective Cost Function for Video Watermarking

Multimedia Research ◽

10.46253/j.mr.v2i4.a4 ◽

2019 ◽

Vol 2 (4) ◽

Keyword(s):

Wavelet Transform ◽

Cost Function ◽

Haar Wavelet ◽

Video Watermarking ◽

Haar Wavelet Transform

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A Novel Scene-Based Video Watermarking Scheme for Copyright Protection

Journal of Intelligent Systems ◽

10.1515/jisys-2017-0039 ◽

2018 ◽

Vol 27 (1) ◽

pp. 47-66 ◽

Cited By ~ 5

Author(s):

Dolley Shukla ◽

Manisha Sharma

Keyword(s):

Bit Error Rate ◽

Error Rate ◽

Copyright Protection ◽

Performance Metrics ◽

Wide Spectrum ◽

Similarity Index ◽

Haar Wavelet ◽

Video Watermarking ◽

Computational Time ◽

Discrete Wavelet

Abstract Many illegal copies of original digital videos are being made, as they can be replicated perfectly through the Internet. Thus, it is extremely necessary to protect the copyrights of the owner and prevent illegal copying. This paper presents a novel approach to digital video watermarking for copyright protection using two different algorithms, whereby successive estimation of a statistical measure was used to detect scene boundaries and watermark was embedded in the detected scenes with discrete wavelet transform. Haar wavelet was used for decomposition. For embedding, the approaches used were (i) the detailed subband (LH subband) and (ii) the approximate subband (LL subband) of the cover video. Imperceptibility, robustness, and channel capacity were measured using both algorithms. The system was tested for robustness in the presence of 15 different attacks of five different categories, and, under multiple attacks, ensured that a wide spectrum of attack analysis has been done. The performance metrics measured included mean square error, peak signal-to-noise ratio, structural similarity index, normalized correlation, and bit error rate. The experimental results demonstrated the better visual imperceptibility and improved performance in terms of normalized correlation and bit error rate with embedding using the LL subband. Comparative analysis with existing schemes proved the improved robustness, better imperceptibility, and reduced computational time of both the proposed schemes.

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