Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
Data Embedding Based on Better Use of Bits in Image Pixels

Authors: Rehab H. Alwan, Fadhil J. Kadhim, Ahmad T. Al-Taani

Abstract:

In this study, a novel approach of image embedding is introduced. The proposed method consists of three main steps. First, the edge of the image is detected using Sobel mask filters. Second, the least significant bit LSB of each pixel is used. Finally, a gray level connectivity is applied using a fuzzy approach and the ASCII code is used for information hiding. The prior bit of the LSB represents the edged image after gray level connectivity, and the remaining six bits represent the original image with very little difference in contrast. The proposed method embeds three images in one image and includes, as a special case of data embedding, information hiding, identifying and authenticating text embedded within the digital images. Image embedding method is considered to be one of the good compression methods, in terms of reserving memory space. Moreover, information hiding within digital image can be used for security information transfer. The creation and extraction of three embedded images, and hiding text information is discussed and illustrated, in the following sections.

Keywords: Image embedding, Edge detection, gray level connectivity, information hiding, digital image compression.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1083177

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1771

References:


[1] Rochester & Xerox Corporation. Reversible data hiding, IEEE ICIP 2002.
[2] Ilt Arnold Baldoza and Mr. Michael Sieffert, Methods for Detecting Tampering in Digital Images, TECH CONNECT, Reference document, IF-99-05.
[3] S. Walton, Information Authentication for a Slippery New age, Dr. Dobbs Journal, April 1995, Vol. 20, No. 4, pp. 18-26.
[4] Gonzalez & Woods digital image processing, Second edition, 2001.
[5] R. T Yeh and S. Y. Beng, Fuzzy relations, fuzzy graphs, and their applications to clustering analysis, L. A. Zadeh, K. S. Fu and M Shimura, Eds. New York: Academic, 1975.
[6] Rosenfeld, Fuzzy digital topology, Inform. Control, Vol. 40, pp. 76- 87, 1979.
[7] Rosenfeld, On connectivity properties of grayscale pictures, Pattern Recognition, Vol. 16, pp. 47-50, 1983.
[8] Rosenfeld, the fuzzy geometry of image subsets. Pattern Recognition Letters, Vol. 2, pp, 311-317, 1984.
[9] Bloch, Fuzzy connectivity and mathematical morphology, Pattern Recognition Letters, Vol. 14, pp. 483-488, 1993.
[10] S. Dellepiane and F.Fontana, Extraction of intensity connectedness for image processing, Pattern Recognition Letters, Vol. 16, pp. 313- 324, 1995.
[11] M.Lifshitz and S.M. Pizer, A multi resolution hierarchical approach to image segmentation based on intensity extrema, IEEE Trans. Pattern Anal. Machine Intel, Vol. 12 pp.529-540, 1990.
[12] J. J. Koenderink, The structure of images, Biol,Cybern, Vol. 50, pp. 360-370, 1984.
[13] Anderson, R. J., Ed. Information hiding terminology, Vol. 1174, lecture notes in computer science, Springer, 1996.
[14] Henk Heijmans and Lute Kamstra, Reversible data embedding based on the Haar Wavelet decomposition, proc. Vol. 11, Digital Image computing Techniques and application, 10-12 Dec. 2003, Sydney.