Earticle

초록

영어

In order to distinguish the sharpening operation in a manipulated image, a sharpen forgeries detection algorithm is proposed. Combined with efficient image signal processing method, a new non-subsampled dyadic contourlet transform based on contrast à trous wavelet is designed. In contourlet domain, sharpening characteristics are analyzed by multi-scale and multi-directional methods. A classifier is trained by artificial intelligence methods to complete sharpening tampered image detection. Experimental results show that the algorithm in this paper can detect possible sharpening and locate the tampering boundary accurately.

목차

Abstract
 1. Introduction
 2. Dyadic Contrast Contourlet Transform
  2.1. Dyadic Wavelet
  2.2. Dyadic Contrast Contourlet Transform
 3. Amplification and Locating of the Sharpen Tampering
  3.1. Extracted Sharpen Characteristics in Dyadic Contrast Contourlet Transform
  3.2. Artificial Intelligence Methods to Position of the Sharpening Tampered Regions
 4. Experimental Results and Analysis
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Hua Zhen [ Shandong Institute of Business and Technology, Yantai, China ]
• Li Jinjiang [ Shandong Institute of Business and Technology, Yantai, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605