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Shadow Image Based Reversible Data Hiding Using Addition and Subtraction Logic on the LSB Planes

Publication Type : Journal Article

Source : Sensing and Imaging, Springer, 22:7, 2021.

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Campus : Amaravati

School : School of Computing

Year : 2021

Abstract : Image steganographic communication demands a fair trade-off among the three diametrically opposed metrics such as higher capacity, larger visual quality, and attack survival ability (ASA). Recently, some reversible data hiding (RDH) techniques using dual images have shown promising results to achieve the aforementioned needs. However, maintaining a balance among these metrics is still an open challenge. In this paper, using the concept of shadow image, which is basically the replica of the cover image (CI) and performing some simple addition and subtraction logic on the shadow image pixels, we propose an improved RDH technique that offers larger capacity, better stego-image (SI) quality and higher ASA. At first, during embedding, three shadow images of the CI are produced. Then, the shadow image pixels are adjusted based on their XOR features of the least significant bit planes. After embedding the secret bits, a maximum of ± 1 modification has been observed in the SI pixels. Later at the receiving end, the CI has been restored by applying the round function on the obtained SI pixels. Experimental results show that the proposed technique offers excellent visual quality with peak signal-to-noise ratio and structural similarity index (SSIM) of 52.47 dB, 53.91 dB, 52.48 dB and 0.9974, 0.9981, 0.9974 for the respective shadow images. Further, the proposed technique show exceptional anti-steganalysis ability to regular and singular analysis, pixel difference histogram analysis, and bit pair analysis. Additionally, the proposed technique successfully avoids the falling-off boundary problem.

Cite this Research Publication : M. Sahu, N. Padhy, S. S. Gantayat, A. K. Sahu, "Shadow Image Based Reversible Data Hiding Using Addition and Subtraction Logic on the LSB Planes," Sensing and Imaging, Springer, 22:7, 2021.

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