• Title/Summary/Keyword: 랜덤 투영

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Vector Data Hashing Using Line Curve Curvature (라인 곡선 곡률 기반의 벡터 데이터 해싱)

  • Lee, Suk-Hwan;Kwon, Ki-Ryong
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.36 no.2C
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    • pp.65-77
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    • 2011
  • With the rapid expansion of application fields of vector data model such as CAD design drawing and GIS digital map, the security technique for vector data model has been issued. This paper presents the vector data hashing for the authentication and copy protection of vector data model. The proposed hashing groups polylines in main layers of a vector data model and generates the group coefficients by the line curve curvatures of the first and second type of all poly lines. Then we calculate the feature coefficients by projecting the group coefficients onto the random pattern and generate finally the binary hash from the binarization of the feature coefficients. From experimental results using a number of CAD drawings and GIS digital maps, we verified that the proposed hashing has the robustness against various attacks and the uniqueness and security by the random key.

Wavelet-Based Digital Watermarking Using Level-Adaptive Thresholding (레벨 적응적 이치화를 이용한 웨이블릿 기반의 디지털 워터마킹)

  • Kim, Jong-Ryul;Mun, Young-Shik
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.37 no.1
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    • pp.1-10
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    • 2000
  • In this paper, a new digital watermarking algorithm using wavelet transform is proposed. Wavelet transform is widely used for image processing, because of its multiresolution characteristic which conforms to the principles of the human visual system(HVS). It is also very efficient for localizing images in the spatial and frequency domain. Since wavelet coefficients can be characterized by the gaussian distribution, the proposed algorithm uses a gaussian distributed random vector as the watermark in order to achieve invisibility and robustness. After the original image is transformed using DWT(Discrete Wavelet Transform), the coefficients of all subbands including LL subband are utilized to equally embed the watermark to the whole image. To select perceptually significant coefficients for each subband, we use level-adaptive thresholding. The watermark is embedded to the selected coeffocoents, using different scale factors according to the wavelet characteristics. In the process of watermark detection, the similarity between the original watermark and the extracted watermark is calculated by using vector projection method. We analyze the performance of the proposed algorithm, compared with other transform-domain watermarking methods. The experimental results tested on various images show that the proposed watermark is less visible to human eyes and more robust to image compressions, image processings, geometric transformations and various noises, than the existing methods.

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