General Principles of Integrity Checking of Digital Images and Application for Steganalysis

Open access


The new common approach for integrity checking of digital images is developed. The new features of formal parameters defining image are revealed, theoretically grounded and practically tested. The characteristics of the mutual arrangement of left and right singular vectors corresponding to the largest singular value of the image’s matrix (block of matrix) and the vector composed of singular numbers is obtained. Formal parameters are obtained using normal singular decomposition of matrix (block of matrix) which is uniquely determined. It is shown that for most blocks of original image (no matter lossy or lossless) the angle between the left (right) mentioned singular vector and vector composed of singular numbers is defined by the angle between the n-optimal vector and the vector of standard basis of the range corresponding dimension. It is shown that the determined feature brakes for the mentioned formal parameters in a non-original image. This shows the integrity violation of the image, i.e. the existence of the additional information embedded using steganography algorithms. So this can be used as a basis for development of new universal steganography methods and algorithms, and one example of the realization is proposed. The efficiency of the proposed algorithm won’t depend on the details of steganography method used for embedding. All the obtained results can be easily adapted for the digital video and audio analysis.

1. Rey, C. and Dugelay, J.-L. (2002) A survey of watermarking algorithms for image authentication. EURASIP Journal on Advances in Signal Processing, 6, 613-621. DOI: 10.1155/S1110865702204047.

2. Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Del Tongo, L. and Serra, G. (2013) Copy-move forgery detection and localization by means of robust clustering with J-Linkage. Signal Processing: Image Communication, 28(6), 659-669. DOI: 10.1016/j.image.2013.03.006.

3. Farid, H. (2009) Image forgery detection. IEEE Signal Processing Magazine, 26(2), 16-25. DOI: 10.1109/MSP.2008.931079.

4. Gul, G. and Kurugollu, F. (2010) SVD-based universal spatial domain image steganalysis. IEEE Transactions on Information Forensics and Security, 5(2), 349-353. DOI: 10.1109/TIFS.2010.2041826.

5. Bobok, I.I. and Kobozeva, A.A. (2011) Steganalysis as a special case of the analysis of the information system. Suchasna Spetsialna Tekhnika, 1, 21-34.

6. Natarajan, V. and Anitha, R. (2012) Blind image steganalysis based on contourlet transform. International Journal on Cryptography & Information Security, 2(3), 77-87. DOI: 10.5121/ijcis.2012.2307.

7. Kobozeva, A.A. and Khoroshko, V.A. (2009) Analysis of Information Safety. Kyiv: DUT.

8. Kobozeva, A.A. (2014) A basis of common approach to the development of universal steganalysis methods for digital images. Odes’kyi Politechnichnyi Universytet. Pratsi, 2, 136-146. DOI: 10.15276/opu.2.44.2014.25.

9. Koch, E. and Zhao, J. (1995) Towards Robust and Hidden Image Copyright Labeling. In: Proc. of 1995 IEEE Workshop on Nonlinear Signal and Image Processing, Neos Marmaras, Greece, June 1995. Neos Marmaras, Greece: IEEE CAS and ASSP Societies, pp. 123-132.

10. Kutter, M., Jordan, F. and Bossen, F. (1998) Digital signature of color images using amplitude modulation. Journal of Electronic Imaging, 7(2), 326-332.

11. Xia, Z., Yang, L., Sun, X., Liang, W., Sun, D. and Ruan, Z. (2011) A Learning-Based Steganalytic Method against LSB Matching Steganography. Radioengineering, 20(1), 102-109.

12. Li, B., He, J., Huang, J. and Shi, Y.Q. (2011) A survey on image steganography and steganalysis. Journal of Information Hiding and Multimedia Signal Processing, 2(2), 142-172.

13. Fridrich, J. (2004) Feature-Based Steganalysis for JPEG Images and its Implications for Future Design of Steganographic Schemes. In: 6th International Workshop, IH 2004, Toronto, Canada, May 2004. Berlin: Springer, pp. 67-81.

Transport and Telecommunication Journal

The Journal of Transport and Telecommunication Institute

Journal Information

Cite Score 2017: 1.21

SCImago Journal Rank (SJR) 2017: 0.294
Source Normalized Impact per Paper (SNIP) 2017: 1.539


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 163 163 13
PDF Downloads 87 87 5