Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Network image copyright real-time distinguishing method

A network image and copyright technology, applied in the field of information security, can solve the problems of difficult to completely prevent passive Internet attacks, interfere with copyright identification, and the original watermark cannot be used to identify copyright.

Inactive Publication Date: 2011-10-05
ZHEJIANG GONGSHANG UNIVERSITY
View PDF1 Cites 21 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Then, the embedding end transmits the original watermark or part of its information to the detection end (or a third-party notary center) for storage, which requires a certain amount of transmission and storage costs, and it is difficult to completely prevent passive attacks that are common on the Internet during the transmission process.
For example, if an attacker successfully "eavesdrops" on the transmitted original watermark or part of the information of the original watermark, the embedded watermark can be obtained through analysis, and the watermark can be further forged and passed to the detection end, which will make the real original watermark unable to be used to identify the copyright, reaching Interfere with the purpose of copyright identification, making the watermark algorithm unable to resist interpretation attacks [12]

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Network image copyright real-time distinguishing method
  • Network image copyright real-time distinguishing method
  • Network image copyright real-time distinguishing method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0181] 1 Description of Experimental Parameters

[0182] Three grayscale images of Lena, Barbara and Elain with a size of 512×512 in 256 levels are selected as the experimental images, as shown in Image 6 , 7 and 8 are shown. The size of the sub-blocks is 16×16, so the length of the feature watermark W is 1024bit. Logistic chaos map initial value x 0 The value is 0.28, the parameter γ is 1.5, and the first κ=200 random numbers are discarded when the Logistic chaotic sequence encrypts the characteristic watermark W. Encrypted feature watermark W e Adaptively self-embed the DCT coefficients of each sub-block in the (3, 5) and (4, 4) positions of the original Lena, Barbara and Elain images. The scale factor μ of the adaptive embedding is taken as 0.028. The resulting watermarked Lena, Barbara and Elain images are shown in Figure 9 , 10 and 11, the PSNRs between them and the original Lena, Barbara and Elain images are 35.9595, 36.1211 and 35.7637. Therefore, good invis...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a network image copyright real-time distinguishing method. Copyright protection is necessary for digital images during network communication, but the existing robust digital watermarking technology can not achieve the real-time copyright distinguishing. The method comprises the following steps: partitioning an original image to sub-blocks which are not overlapped with each other at an embedded end; processing each sub-block by discrete cosine transformation; generating a feature watermark by comparing the DC coefficient of each sub-block with the average DC coefficient of all sub-blocks; encrypting the Logistic chaotic sequence of the feature watermark; adjusting the magnitudes of two low-frequency coefficients of the discrete cosine transformation of each sub-blockand self-embedding the encrypted feature watermark; and processing each sub-block by inverse discrete cosine transformation to obtain images with watermarks. The method has high robustness to attack.Substantially, the method provided by the invention can achieve complete blind detection by combining the self-embedding encrypted feature watermark with watermark blind extraction and authentication, thereby achieving real-time copyright distinguishing of network images.

Description

technical field [0001] The invention relates to the field of information security. The invention designs a method for real-time identification of network image copyright, which can identify the copyright of digital images transmitted on the network in real time. Background technique [0002] The dissemination of digital images on the Internet is likely to cause copyright disputes, and copyright protection is required. Robust digital watermarking technology can be used to protect the copyright of digital images transmitted on the network. [0003] According to the content of watermarks, watermarks can be divided into meaningful watermarks and meaningless watermarks. Meaningful watermarks generally mean that the content of the watermark has specific meanings, such as binary images, grayscale images, trademarks, author's personal information, etc.; meaningless watermarks generally correspond to a pseudo-random sequence with no specific meaning. In addition, hot springs, etc....

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06T1/00G06F21/00G06F21/10
Inventor 叶天语
Owner ZHEJIANG GONGSHANG UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products