Journal Title : International Journal of Modern Trends in Engineering and Science


Author’s Name : Mr J S Sujin | P Keerthika | V M Mahasrishwari | C Geethaunnamed

Volume 04 Issue 04 2017

ISSN no:  2348-3121

Page no: 33-36

Abstract – In this paper proposes about the digital image to hide or to remove some meaningful or useful information of the image. It is very hard to identify the manipulated image from the original. Hence, it is important to develop such a method which can detect the manipulated one. The detection of a tampering in image is to provide authenticity and to maintain integrity of the image Tamper detection. Image editing software’s are available in low-cost, the originality of digital images can no longer be taken for granted. Digital images are used as cover data for transmitting secret information in the field of steganography. In this paper, we introduce a new set of features for multimedia forensics to find that digital image is an original camera output or it is tampered or embedded with hidden data. We identify image forensic analysis providing three sets of statistical noise features, including those from denoising operations, wavelet analysis, and neighborhood prediction. Our experimental outputs analyse that the proposed method can effectively distinguish digital images from their tampered or stego versions.

Keywords— Zernike moments,Luminanace and Chrominance,Image Tampering,Feature extraction


  1. Monga, A. Banerjee, and B. L. Evans, “A clustering based approach to perceptual image hashing,” IEEE Trans. Inf. Forensics Security, vol. 1, no. 1, pp. 68–79, Mar. 2006.
  2. S. Xiang, H. J. Kim, and J. Huang, “Histogram-based image hashing scheme robust against geometric deformations,” in Proc. ACM Multimedia and Security Workshop, New York, 2007, pp. 121–128
  3. Z. Tang, S. Wang, X. Zhang, W. Wei, and S. Su, “Robust image hashing for tamper detection using non-negative matrix factorization,” J. Ubiquitous Convergence Technol., vol. 2, no. 1, pp. 18–26, May 2008
  4. A. Swaminathan, Y. Mao, and M. Wu, “Robust and secure image hashing,” IEEE Trans. Inf. Forensics Security, vol. 1, no. 2, pp. 215–230, Jun. 2006
  5. Y. Lei, Y. Wang, and J. Huang, “Robust image hash in Radon transform domain for authentication,” Signal Process.: Image Commun., vol. 26, no. 6, pp. 280–288, 2011.
  6. F. Khelifi and J. Jiang, “Perceptual image hashing based on virtual watermark detection,” IEEE Trans. Image Process., vol. 19, no. 4, pp. 981–994, Apr. 2010.
  7. V. Monga and M. K. Mihcak, “Robust and secure image hashing via non-negative matrix factorizations,” IEEE Trans. Inf. Forensics Security, vol. 2, no. 3, pp. 376–390, Sep. 2007
  8. Z. Tang, S. Wang, X. Zhang, W. Wei, and Y. Zhao, “Lexicographical framework for image hashing with implementation based on DCT and NMF,” Multimedia Tools Applicat., vol. 52, no. 2–3, pp. 325–345, 2011.
  9. F. Ahmed, M. Y. Siyal, and V. U. Abbas, “A secure and robust hashbased scheme for image authentication,” Signal Process., vol. 90, no. 5, pp. 1456–1470, 2010
  10. X. Lv and Z. J. Wang, “Perceptual image hashing based on shape contexts and local feature points,” IEEE Trans. Inf. Forensics Security, vol. 7, no. 3, pp. 1081–1093, Jun. 2012.