A Lossless Data Hiding Technique using Secure LSB in Images
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Abstract
In this work we have implemented a lossless data hiding technique based on Least Significant Bit Embedding algorithm. Any image based data hiding method using LSB embedding sacrifices at least 12.5% accuracy in the image that is used as carrier. In this work we have come out with a better approach which results in better image quality and increases the amount of hidden data.
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References
Fridrich, J., Goljan, M., & Du, R. (2001). Reliable detection of LSB steganography in color and grayscale images. Proceedings of the 2001 workshop on Multimedia and security new challenges - MM&Sec ’01, 27. New York, New York, USA: ACM Press. doi:10.1145/1232454.1232466 [2] Gonzalez, Rafael C., and Paul A. Wintz. "Image Compression Standards." Digital Image Processing. 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 2002. 492-510. Print. [3] Lyu, S., & Farid, H. (2006). Steganalysis using higherorder image statistics. Forensics and Security, IEEE Transactions on, 1(1), 111-119. [4] Muñoz, A. (2007). XStegSecret beta v0.1. Retrieved from http://stegsecret.sourceforge.net/index.html
Sandeep Sharma et al, International Journal of Advanced Research in Computer Science, 9 (Special Issue III), May 2018, 351-354
Conference Paper: Third National Conference on “Advances in Computing and Information Technology†Organized by: School of Computing and Information Technology, REVA University, Bengaluru, India 354
Provos, N. (2001). Detecting steganographic content on the internet. Ann Arbor. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=i ntitle:Detecting+Steganographic+Content+on+the+Internet# 0 [6] Rocha, A., Scheirer, W., & Boult, T. (2011). Vision of the unseen: Current trends and challenges in digital image and video forensics. ACM Computing Surveys. Retrieved from http://dl.acm.org/citation.cfm?id=1978805
Węgrzyn, M. Virtual Steganographic Laboratory for Digital Images (VSL). Retrieved from http://vsl.sourceforge.net/ [8] Westfeld, A. (2001). F5 — A Steganographic Algorithm High Capacity Despite Better Steganalysis, 289-302. [9] Westfeld, A., & Pfitzmann, A. (n.d.). Attacks on Steganographic Systems: Breaking the Steganographic Utilities EzStego, Jsteg, Steganos , and S-Tools — and Some Lessons Learned, 1-16.