High capacity data hiding with absolute moment block truncation coding image based on interpolation

Cheonshik Kim; Dongkyoo Shin; Ching-Nung Yang; Cheonshik Kim; Dongkyoo Shin; Ching-Nung Yang

doi:10.3934/mbe.2020009

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 1: 160-178. doi: 10.3934/mbe.2020009

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Research article Special Issues

High capacity data hiding with absolute moment block truncation coding image based on interpolation

1.
Department of Computer Engineering, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul 143-747, South Korea
2.
Department of Computer Science and Information Engineering, National Dong Hwa University, No. 1, Sec. 2, Da Hsueh Rd., Shoufeng, Hualien 97401, Taiwan

Received: 13 May 2019 Accepted: 29 August 2019 Published: 26 September 2019

Data hiding is a way of hiding secret data on cover-media and it is used for a variety of applications. An important of the data hiding is to conceal the data in a secret way without loss of cover-media. Until now, continuous research on absolute moment block truncation coding based data hiding methods have improved a performance on data concealment and image quality. However, the current absolute moment block truncation coding based data hiding technology has a limitation in deriving a method that significantly surpasses existing performance. In this paper, we propose a new method to overcome this problem. To do this, first the original image is transformed to the cover image using absolute moment block truncation coding and is expanded the image using neighbor average interpolation algorithm. The proposed three data hiding methods are based on the generated cover image. The first method is to directly replace the pixel value, which is a component of each block, with the same secret value. The second method is to replace the pixels to match the secret bits only for the extended pixels in each block of the cover image. The third method is to apply Hamming code to each block to minimize the number of replacement pixels for data hiding. Experimental results show that our method is superior in terms of efficiency compared to traditional absolute moment block truncation coding based data hiding methods.

Keywords:

Citation: Cheonshik Kim, Dongkyoo Shin, Ching-Nung Yang. High capacity data hiding with absolute moment block truncation coding image based on interpolation[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 160-178. doi: 10.3934/mbe.2020009

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Abstract

References

[1]	W. Bender, D. Gruhl, N. Morimoto, et al., Techniques for data hiding, IBM Syst. J., 35 (1996), 313-336.
[2]	C. N. Yang, S. C. Hsu and C. Kim, Improving stego image quality in image interpolation based data hiding, Comput. Stand. Interfaces, 50 (2017), 209-215.
[3]	C. Kim, D. Shin, C. N. Yang, et al., Improving capacity of Hamming (n,k)+1 stego-code by using optimized Hamming+k, Digital Signal Process., 78 (2018), 284-293.
[4]	Y. Q. Shi, X. Li, X. Zhang, et al., Reversible data hiding: Advances in the past two decades, IEEE Access, 4 (2016), 3210-3237.
[5]	F. Huang, X. Qu, H. J. Kim, et al., Reversible Data Hiding in JPEG Images, IEEE Trans. Circuits Syst. Video Technol., 26 (2016), 1610-1621.
[6]	J. Mielikainen, LSB matching revisited, IEEE Signal Proc. Let., 13 (2006), 285-287.
[7]	C. K. Chan and L. M. Cheng, Hiding data in images by simple LSB substitution, Pattern Recognit., 37 (2004), 469-474.
[8]	C. C. Chang, C. C. Lin, C. S. Tseng, et al., Reversible hiding in DCT-based compressed images, Inf. Sci., 177 (2007), 2768-2786.
[9]	C. C. Chang, T. S. Chen and L. Z. Chung, A steganographic method based upon JPEG and quantization table modification, Inf. Sci., 141 (2002), 123-138.
[10]	C. Bergman and J. Davidson, Unitary embedding for data hiding with the SVD, Proceedings Volume 5681, Security, Steganography, and Watermarking of Multimedia Contents VⅡ, (2005). Available from: https://doi.org/10.1117/12.587796.
[11]	E. Delp and O. Mitchell, Image compression using block truncation coding, IEEE Trans. Commun., 27 (1979), 1335-1342.
[12]	W. Hong, T. S. Chen and C. W. Shiu, Lossless steganography for AMBTC compressed images, 2008 Congress on Image and Signal Processing, 2 (2008), 13-17. Available from: https://ieeexplore.ieee.org/document/4566259. doi: 10.1109/CISP.2008.638
[13]	J. C. Chuang and C. C. Chang, Using a simple and fast image compression algorithm to hide secret information, Int. J. Comput. Appl., 28 (2006), 329-333.
[14]	J. Chen, W. Hong, T. S. Chen, et al., Steganography for BTC compressed images using no distortion technique, Imaging Sci. J., 58 (2010), 177-185. doi: 10.1179/136821910X12651933390629
[15]	W. Hong, J. Chen, T. S. Chen, et al., Steganography for block truncation coding compressed images using hybrid embedding scheme, Int. J. Innov. Comput. Inf. Control, 7 (2011), 733-743.
[16]	D. Ou and W. Sun, High payload image steganography with minimum distortion based on absolute moment block truncation coding, Multimed. Tools Appl., 74 (2015), 9117-9139.
[17]	J. Bai and C. C. Chang, A high payload steganographic scheme for compressed images with hamming code, Int. J. Netw. Secur., 18 (2016), 1122-1129.
[18]	Y. H. Huang, C. C. Chang and Y. H. Chen, Hybrid secret hiding schemes based on absolute moment block truncation coding, Multimedia Tools Appl., 76 (2017), 6159-6174.
[19]	W. Hong, Efficient data hiding based on block truncation coding using pixel pair matching technique, Symmetry, 10 (2018), 1-8.
[20]	W. Hong, and T. S. Chen, A novel data embedding method using adaptive pixel pair matching, IEEE Trans. Inf. Forensics Secur., 7 (2012), 176-184.
[21]	K. H. Jung and K. Y. Yoo, Data hiding method using image interpolation, Comput. Stand. Interfaces, 31 (2009), 465-470.
[22]	Z. Wang, A. C. Bovik, H. R. Sheikh, et al., Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process., 13 (2004), 600-612.

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