Abstract
Digital image watermarking is defined as inserting digital signals in to a cover image such that the degradation of quality would be minimized and most amounts of the hidden data can be retrieved after geometric and signal processing distortions. In order to select an efficient algorithm in digital image watermarking to fulfill the criteria such as robustness, imperceptibility and capacity, it is necessary to be aware of the specifications of the chosen method. Considering the independency of image histogram from the position of the pixels classifies the histogram modification based watermarking as an appropriate method against geometric and signal processing attacks. This paper investigates the recent presented methods in histogram modification based image watermarking from 2010 to 2017 to identify the weak and strength points of them to emphasize which method should be developed to enhance the performance of the watermarking algorithms in terms of the mentioned criteria. Results show that using the techniques like selection of the adjacent bins intelligently, secret keys and constant points of cover images make them to be a good candidate for image watermarking to withstand against geometric and signal processing attack.
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References
Araghi, T.K., et al.: Taxonomy and performance evaluation of feature based extraction techniques in digital image watermarking (2016)
Araghi, T.K., Manaf, A.B.A.: Evaluation of digital image watermarking techniques. In: International Conference of Reliable Information and Communication Technology, pp. 361–368 (2017)
Araghi, T.K., et al.: A survey on digital image watermarking techniques in spatial and transform domains (2016)
Araghi, S.K., et al.: Power of positive and negative thoughts extracted from EEG signals to find a biometric similarity. In: 6th SASTech 2012, Malaysia, Kuala Lumpur, 24–25 March 2012
He, X., et al.: A geometrical attack resistant image watermarking algorithm based on histogram modification. Multidimension. Syst. Signal Process. 26, 291–306 (2015)
Nasir, I., et al.: Robust image watermarking via geometrically invariant feature points and image normalisation. Image Process. IET 6, 354–363 (2012)
Licks, V., Jordan, R.: Geometric attacks on image watermarking systems. IEEE Multimedia 12, 68–78 (2005)
Xiang, S., et al.: Invariant image watermarking based on statistical features in the low-frequency domain. IEEE Trans. Circ. Syst. Video Technol. 18, 777–790 (2008)
Stark, J.A.: Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans. Image Process. 9, 889–896 (2000)
Araghi, T.K., Manaf, A.A.: Template based methods in image watermarking to avoid geometric attacks. In: International Conference on Research and Innovation in Computer Engineering and Computer Sciences (RICCES) (2018)
Deng, C., et al.: Local histogram based geometric invariant image watermarking. Sig. Process. 90, 3256–3264 (2010)
Pun, C.-M., Yuan, X.-C.: Histogram modification based image watermarking resistant to geometric distortions. Multimed. Tools Appl. 74, 7821–7842 (2015)
Juang, Y.-S., et al.: Histogram modification and wavelet transform for high performance watermarking. Math. Probl. Eng. 2012, 14 pages (2012)
Divya, M., Kamalesh, M.D.: Recovery of watermarked image from geometrics attacks using effective histogram shape based index. Indian J. Sci. Technol. 9 (2016)
Hu, X., Wang, D.: A histogram based watermarking algorithm robust to geometric distortions (2015)
Salunkhe, P.P., Kanse, Y.: Reversible image watermarking based on histogram shifting (2017)
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Araghi, T.K. (2019). Digital Image Watermarking and Performance Analysis of Histogram Modification Based Methods. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 858. Springer, Cham. https://doi.org/10.1007/978-3-030-01174-1_49
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DOI: https://doi.org/10.1007/978-3-030-01174-1_49
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