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DC Field | Value | Language |
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dc.contributor.author | SISODIA, VINOD KUMAR | - |
dc.date.accessioned | 2017-12-13T17:12:35Z | - |
dc.date.available | 2017-12-13T17:12:35Z | - |
dc.date.issued | 2016-07 | - |
dc.identifier.uri | http://dspace.dtu.ac.in:8080/jspui/handle/repository/16100 | - |
dc.description.abstract | The recent advent in the field of multimedia proposed a many facilities in transport, transmission and manipulation of data. Along with this advancement of facilities there are larger threats in authentication of data, its licensed use and protection against illegal use of data. A lot of digital image watermarking techniques have been designed and implemented to stop the illegal use of the digital multimedia images. This paper compares the different attacks on extracted watermark. The comparison (PSNR, MSE, NC) of original image v/s watermarked image and extracted watermarked image before and after various attacks of the watermarked images has been calculated and verified on the parameters of PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error) and NC (Normal Correlation) In this paper the image is watermarked using native prediction in distinction enlargement reversible watermarking. For every pixel, a least square predictor is computed on a block focused on the pixel and also the corresponding prediction error is distended. An equivalent predictor is recovered at detection with none further data. Experimental results are provided. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartofseries | TD-2770; | - |
dc.subject | REVERSIBLE WATERMARKING | en_US |
dc.subject | ADAPTIVE PREDICTION | en_US |
dc.subject | SALT AND PEPPER NOISE | en_US |
dc.subject | GAUSSIAN NOISE | en_US |
dc.subject | WIENER FILTER | en_US |
dc.title | LOCAL PREDICTION BASED DIFFERENCE EXPANSION REVERSIBLE WATERMARKING | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | M.E./M.Tech. Electronics & Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
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VINOD KUMAR SISODIA M.TECH(2K13_SPD_26).pdf | 1.86 MB | Adobe PDF | View/Open |
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