Abstract
Achieving high embedding capacities for information hiding systems while maintaining high perceptual stego quality is a critical challenge in steganography. This quandary is attracting researchers to overcome the trade-off barrier between high capacities and enhanced levels of stego image quality. This work introduces a promising transform-domain hiding scheme that aims to achieve ultimate hiding capacity with premium perceptual quality results. The proposed scheme is based on the fact that highly correlated images are represented by significant coefficients that are strongly packed in the transform-domain of the image. This allows for a large space in the insignificant coefficient areas to embed in. To exploit this feature optimally, a curve-fitting approach is introduced and implemented in various adaptive-region transform-domain embedding schemes. Experimental results demonstrate that this curve-fitting methodology is able to enhance adaptive transform-domain embedding schemes where very high embedding capacities can be achieved that are much higher than competing high-capacity hiding schemes. The other noticeable result is that although the embedding capacity has increased compared to earlier work, the perceptual quality level has also improved over previous methods.
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Acknowledgments
The authors would like to thank the five anonymous reviewers for their valuable suggestions that helped improve the original manuscript. This work was supported by the College of Graduate Studies and Research at the University of Sharjah.
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Rabie, T., Kamel, I. & Baziyad, M. Maximizing embedding capacity and stego quality: curve-fitting in the transform domain. Multimed Tools Appl 77, 8295–8326 (2018). https://doi.org/10.1007/s11042-017-4727-5
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DOI: https://doi.org/10.1007/s11042-017-4727-5