Watermarked and Noisy Images identification Based on Statistical Evaluation Parameters

Sattar B. Sadkhan, Nidaa A. Abbas

College of IT- University of Babylon  

DOI: https://doi.org/10.17656/jzs.10265

Abstract: 

A watermark scheme is an important technique for copyright protection of digital images. Digital watermarking is the process of computer-aided information hiding in a carrier signal. The main interest of this paper is copyright protection, and it takes into consideration four important aspects: (i) Implementation the images watermarking by Least Significant Bit method (LSB) for JPEG gray images using invisible watermark, (ii) Evaluation the watermarking images using different statistical parameters, (iii) Identifying watermark images from noisy images by showing that the difference in results using open set identification, (iv) Proposing threshold equations that can be used to differentiate among noisy and watermarked images based on the used statistical parameters of the tested images. By comparing the image quality, obtained by the proposed method with the calculated statistical metrics like Variance, Standard Deviation, Kurtosis and Skewness. The results are promising and give us a great indication to differentiate between the images of watermarking and noisy images. 

References 

1-Zhou Wang, Alan Conrad Bovik, Hamid Rahim Sheikh and Eero P. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural  Similarity”, IEEE TRANSACTIONS ON IMAGE PROCESSING, 4(13) , APRIL (2004). 

2-Christine I. Podilchuk and Edward J. Delp, “Digital Watermarking: Algorithms and Applications”, IEEE SIGNAL PROCESSING MAGAZINE, JULY  (2001) 

3-R. Ahlswede and N. Cai ,”Watermarking Identification Codes with Related Topics on Common Randomness”, Information Transfer and  Combinatorics, LNCS 4123, pp. 107–153, Springer-Verlag Berlin Heidelberg (2006). 

4-R. Ahlswede and G. Dueck, Identification via channels, IEEE Trans. Inform. Theory, 1(35),  pp.15-29, (1989). 

5-Y. Steinberg and N. Merhav, “Indentification in the presence of side information with application to watermarking”, IEEE Trans. Inform.  Theory, (47), pp. 1410– 1422, (2001). 

6-A.G.Borsand I.Pitas, “Image watermarking using DCT domain constraints,” in IEEE Proc. Int. Conf. on Image Processing, Lausanne,  Switzerland, Sept., (3), pp. 231-234, (1996). 

7-Ahmed A. Abdulfetah, Xingming Sun, Hengfu Yang and Nur Mohammad, “Robust Adaptive Image Watermarking using Visual  Models in  DWT and DCT Domain”, Information Technology Journal 9 (3) pp. 460-466, (2010). 

8-Dhandapani Samiappan and Krishnan Ammasi ,“Robust Image Watermarking Using Discrete Wavelet Transform”, Journal of Computer  Science, DOI: 10.3844/jcssp.2011.1.5,   1(7),  pp. 1-5 

9-S. Voloshynovskiy, S. Pereira and T. Pun, “Watermark attacks”, Erlangen Watermarking Workshop 99, Oct., (1999). 

10-Neil F. Johnson, “An Introduction to Watermark Recovery from Images”, SANS Intrusion Detection and Response Conference (IDR'99) held  in San Diego, CA, February 9-13, (1999). 

11-Mir Shahriar Emami and Ghazali Bin Sulong, “Set Removal Attack: A New Geometric Watermarking Attack”, International Conference on  Future Information Technology, IPCSIT, IACSIT Press, Singapore, (13), (2011). 

12-Eugene K. Yen and Roger G. Johnston, “The Ineffectiveness of the Correlation Coefficient for Image Comparisons”,  http://jps.anl.gov/vol.2/3-Correlation.pdf 

13-Joseph Lee Rodgers and W. Alan Nicewander, “Thirteen Ways to Look at the Correlation Coefficient”, The American Statistician, 1(42), pp.  59-66, (1988). 

14-Dr. Eng. Sattar B. Sadkhan, Dr. Nidaa A. Abbas ,” Performance Evaluation of Speech Scrambling Methods Based on Statistical Approach”,  FONDAZIONE GIORGIO RONCHI, 5, Oct., Italy, (2011). 

15-A. Azzalini and A. D. Valle, "The multivariate skew-normal distribution," Biometrika, (83), pp. 715-726, December 1, (1996).  

View Full Article