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Sparse transformation-based digital blind watermark embedding and extracting method

A sparse transformation and blind watermarking technology, applied in character and pattern recognition, image data processing, image data processing, etc., can solve problems such as difficult watermark construction

Active Publication Date: 2018-09-28
SUZHOU UNIV
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  • Abstract
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  • Claims
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AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a method for embedding and extracting a digital blind watermark based on sparse transformation, so as to solve the problem in the prior art that it is difficult to realize watermark construction based on sparse transformation, and at the same time realize the blind extraction of watermark, and provide a more A good digital watermarking solution

Method used

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  • Sparse transformation-based digital blind watermark embedding and extracting method
  • Sparse transformation-based digital blind watermark embedding and extracting method
  • Sparse transformation-based digital blind watermark embedding and extracting method

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Embodiment 1

[0058] Embodiment 1: A method for embedding and extracting a digital blind watermark based on sparse transformation, by performing 8×8 non-overlapping blocks on the original image, combining the divided image blocks with a sparse transformation model, and using sparse frequency domain space compression The embedding range, and then estimate the coefficients of each frequency domain component based on the Alpha distribution, and select the frequency domain components with similar distribution for watermark embedding. When watermark is extracted, the original image is not needed, and blind extraction is realized.

[0059] For ease of understanding, the sparse transformation and Alpha stable distribution models are first introduced as follows:

[0060] 1. Sparse transformation

[0061] Sparse transformation model, assuming the signal after transformation can then be sparsely represented, ,in is sparse coding, e is an error. Unlike usual sparse dictionary learning, W I...

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Abstract

The invention discloses a sparse transformation-based digital blind watermark embedding and extracting method. The method comprises the following steps of: carrying out 8*8 non-overlapped blocking onan original image; combining the segmented image blocks with a sparse transformation model; compressing an embedding range by using a sparse frequency domain space; estimating each frequency domain component coefficient on the basis of Alpha distribution; and carrying out watermark embedding by selection similarly distributed frequency domain components. During watermark extraction, original images are not required so that blind extraction is realized. Experimental results prove that the method is capable of effectively resisting the attacks of JPEG compression, noise and shearing, and has relatively good invisibility and robustness.

Description

technical field [0001] The invention relates to a digital watermark processing method, in particular to a digital watermark embedding and extraction method based on sparse transformation and Alpha stable distribution. Background technique [0002] With the rapid development of information technology and network technology, the storage, reproduction and dissemination of digital multimedia information has become very convenient. The editing, modification, reproduction and distribution of digital music and digital images involve the copyright protection of digital media creators and digital Media information security issues. Piracy and copyright disputes have become increasingly serious social problems. [0003] Digital watermarking technology is a frontier research field emerging in recent years, and it has developed rapidly in copyright protection and integrity authentication of multimedia information. Embedding digital watermarks containing specific copyright information i...

Claims

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Application Information

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IPC IPC(8): G06T1/00G06K9/62G06F21/16
CPCG06F21/16G06T1/0057G06F18/214
Inventor 顾宇鑫马小虎边丽娜
Owner SUZHOU UNIV
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