한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제17권7호
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  • Pages.964-969
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  • 2007
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  • 1976-9172(pISSN)
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  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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특이치 분해와 Fuzzy C-Mean(FCM) 군집화를 이용한 벡터양자화에 기반한 워터마킹 방법

An Watermarking Method Based on Singular Vector Decomposition and Vector Quantization Using Fuzzy C-Mean Clustering

  • 이병희 (명지대 공대 정보공학과) ;
  • 장우석 (명지대 공대 정보공학과) ;
  • 강환일 (명지대 공대 정보공학과)
  • 발행 : 2007.12.25
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초록

본 논문은 원본 영상과 은닉 영상의 좋은 압축률과 만족할만한 이미지의 질, 그리고 외부공격에 강인한 영상 은닉의 한 방법을 제안한다. 이 워터마킹 방법은 특이치 분해와 퍼지 군집화 기반 벡터양자화를 이용한다. 실험에서는 은닉된 영상의 비가시성과 외부공격에 대한 강인성을 증명하였다. 이 워터마킹기법의 장점은 워터마크된 영상이 이미 압축되어 있으므로 압축과정과 동시에 저작권 보호에 이용할 수 있다는 장점이 있다.

In this paper, we propose the image watermarking method for good compression ratio and satisfactory image quality of the cover image and the embedding image. This method is based on the singular value decomposition and the vector quantization using fuzzy c-mean clustering. Experimental results show that the embedding image has invisibility and robustness to various serious attacks. The advantage of this watermarking method is that we can achieve both the compression and the watermarking method for the copyright protection simultaneously.

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참고문헌

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