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.<\/p>\r\n","references":"[1] Rochester & Xerox Corporation. Reversible data hiding, IEEE ICIP\r\n2002.\r\n[2] Ilt Arnold Baldoza and Mr. Michael Sieffert, Methods for Detecting\r\nTampering in Digital Images, TECH CONNECT, Reference\r\ndocument, IF-99-05.\r\n[3] S. Walton, Information Authentication for a Slippery New age, Dr.\r\nDobbs Journal, April 1995, Vol. 20, No. 4, pp. 18-26.\r\n[4] Gonzalez & Woods digital image processing, Second edition, 2001.\r\n[5] R. T Yeh and S. Y. Beng, Fuzzy relations, fuzzy graphs, and their\r\napplications to clustering analysis, L. A. Zadeh, K. S. Fu and M\r\nShimura, Eds. New York: Academic, 1975.\r\n[6] Rosenfeld, Fuzzy digital topology, Inform. Control, Vol. 40, pp. 76-\r\n87, 1979.\r\n[7] Rosenfeld, On connectivity properties of grayscale pictures, Pattern\r\nRecognition, Vol. 16, pp. 47-50, 1983.\r\n[8] Rosenfeld, the fuzzy geometry of image subsets. Pattern Recognition\r\nLetters, Vol. 2, pp, 311-317, 1984.\r\n[9] Bloch, Fuzzy connectivity and mathematical morphology, Pattern\r\nRecognition Letters, Vol. 14, pp. 483-488, 1993.\r\n[10] S. Dellepiane and F.Fontana, Extraction of intensity connectedness\r\nfor image processing, Pattern Recognition Letters, Vol. 16, pp. 313-\r\n324, 1995.\r\n[11] M.Lifshitz and S.M. Pizer, A multi resolution hierarchical approach\r\nto image segmentation based on intensity extrema, IEEE Trans.\r\nPattern Anal. Machine Intel, Vol. 12 pp.529-540, 1990.\r\n[12] J. J. Koenderink, The structure of images, Biol,Cybern, Vol. 50, pp.\r\n360-370, 1984.\r\n[13] Anderson, R. J., Ed. Information hiding terminology, Vol. 1174,\r\nlecture notes in computer science, Springer, 1996.\r\n[14] Henk Heijmans and Lute Kamstra, Reversible data embedding based\r\non the Haar Wavelet decomposition, proc. Vol. 11, Digital Image\r\ncomputing Techniques and application, 10-12 Dec. 2003, Sydney.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 20, 2008"}