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Multi-Image Hiding Blind Robust RGB Steganography in Transform Domain

Multi-Image Hiding Blind Robust RGB Steganography in Transform Domain

Diptasree Debnath, Emlon Ghosh, Barnali Gupta Banik
Copyright: © 2020 |Volume: 15 |Issue: 1 |Pages: 29
ISSN: 1548-1093|EISSN: 1548-1107|EISBN13: 9781799803966|DOI: 10.4018/IJWLTT.2020010102
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MLA

Debnath, Diptasree, et al. "Multi-Image Hiding Blind Robust RGB Steganography in Transform Domain." IJWLTT vol.15, no.1 2020: pp.24-52. http://doi.org/10.4018/IJWLTT.2020010102

APA

Debnath, D., Ghosh, E., & Banik, B. G. (2020). Multi-Image Hiding Blind Robust RGB Steganography in Transform Domain. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 15(1), 24-52. http://doi.org/10.4018/IJWLTT.2020010102

Chicago

Debnath, Diptasree, Emlon Ghosh, and Barnali Gupta Banik. "Multi-Image Hiding Blind Robust RGB Steganography in Transform Domain," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT) 15, no.1: 24-52. http://doi.org/10.4018/IJWLTT.2020010102

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Abstract

Steganography is a widely-used technique for digital data hiding. Image steganography is the most popular among all other kinds of steganography. In this article, a novel key-based blind method for RGB image steganography where multiple images can be hidden simultaneously is described. The proposed method is based on Discrete Cosine Transformation (DCT) and Discrete Wavelet Transformation (DWT) which provides enhanced security as well as improve the quality of the stego. Here, the cover image has been taken as RGB although the method can be implemented on grayscale images as well. The fundamental concept of visual cryptography has been utilized here in order to increase the capacity to a great extent. To make the method more robust and imperceptible, pseudo-random number sequence and a correlation coefficient have been used for embedding and the extraction of the secrets, respectively. The robustness of the method is tested against steganalysis attacks such as crop, rotate, resize, noise addition, and histogram equalization. The method has been applied on multiple sets of images and the quality of the resultant images have been analyzed through various matrices namely ‘Peak Signal to Noise Ratio,' ‘Structural Similarity index,' ‘Structural Content,' and ‘Maximum Difference.' The results obtained are very promising and have been compared with existing methods to prove its efficiency.