Earticle

초록

영어

Forgery detection techniques are required to verify the authenticity of the digital images. The additional noise is the most general way to hide the traces of the tampering done to the image. Original images which do not undergo any alterations are supposed to have a consistency in noise variation. If the image is forged, the noise no longer remains consistent throughout the image. In this paper, a method is proposed to detect the forgery based upon noise estimation and hog feature extraction. The image is first converted to YIQ colorspace, and then the block segmentation is performed on Y component of the YIQ image. Noise is estimated using PCA and hog features are extracted from each block of the image. An unsupervised clustering method is used to cluster the blocks of the image. The experimental results show that the proposed technique detects forged images more effectively as compared to previous method based only on noise estimation.

목차

Abstract
 1. Introduction
 2. Previous Work
 3. Proposed Work
  3.1. Image Pre-Processing
  3.2. Estimation of Noise Variance and Hog Feature Extraction
  3.3. Unsupervised Clustering
  3.4. Refined Classification and Locating the Forged Blocks
 4. Experimental Results and Discussions
 5. Conclusion
 References

키워드

저자

• Mandeep Kaur [ Information Technology, University Institute of Engineering and Technology Panjab University, Chandigarh ]
• Savita Walia [ Information Technology, University Institute of Engineering and Technology Panjab University, Chandigarh ]

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