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Steganography is a method for hidden exchange of information by hiding data in a cover media such as image or sound. Text Steganography is one of the most difficult methods because a text file is not a proper media to hide data in it. In this paper we propose a new text Steganography method. In this method, we hide data in TeX documents. This method hides the data in places where there is a ligature such as ?fi?.

Text-steganography plays an increasingly significant role in covert-communication on Internet. Compared with study on text-steganography, research on text-steganalysis is in its infancy. In this paper, we present a statistical analysis of a kind of word-shift text-steganography by using neighbor difference (length difference of two consecutive spaces). We classify nature PDF documents into two cases serving for our analysis and statistically model word-shift steganographic embedding situations. Our method can reliably detect existence of hidden information. Furthermore, it quantitatively gives estimated embedding rate. The high performance of our method is demonstrated by experiments. Our work will contribute to both text-steganalysis and text-steganography.

This paper presents a new steganography approach suitable for Arabic texts. It can be classified under steganography feature coding methods. The approach hides secret information bits within the letters benefiting from their inherited points. To note the specific letters holding secret bits, the scheme considers the two features, the existence of the points in the letters and the redundant Arabic extension character. We use the pointed letters with extension to hold the secret bit 'one' and the un-pointed letters with extension to hold 'zero'. This steganography technique is found attractive to other languages having similar texts to Arabic such as Persian and Urdu., {"references":["B. Chen and G.W. Wornell, \"Quantization Index Modulation: A Class of\nProvably Good Methods for Digital Watermarking and Information\nEmbedding,\" IEEE Trans. Information Theory, Vol. 47, No. 4, pp. 1423-\n1443, 2001.","M. Hassan Shirali-Shahreza, Mohammad Shirali-Shahreza, \"A New\nApproach to Persian/Arabic Text Steganography,\" 5th IEEE/ACIS\nInternational Conference on Computer and Information Science (ICISCOMSAR\n06), pp. 310- 315, July 2006.","J.C. Judge, \"Steganography: Past, Present, Future\", SANS white paper,\nhttp://www.sans.org/rr/papers/, November 30, 2001.","R. Chandramouli, and N. Memon, \"Analysis of LSB based image\nsteganography techniques\", Proceedings of the International Conference\non Image Processing, Vol. 3, pp. 1019 - 1022, Oct. 2001.","G. Doërr and J.L. Dugelay, \"A Guide Tour of Video Watermarking\",\nSignal Processing: Image Communication, Vol. 18, No 4, pp. 263-282,\n2003.","K. Gopalan, \"Audio steganography using bit modification\", Proceedings\nof the IEEE International Conference on Acoustics, Speech, and Signal\nProcessing, (ICASSP '03), Vol. 2, pp. 421-424, April 2003.","W. Bender, D. Gruhl, N. Morimoto, and A. Lu, \"Techniques for data\nhiding\", IBM Systems Journal, Vol. 35, No 4, pp. 313-336, 1996.","K. Bennett, \"Linguistic Steganography: Survey, Analysis, and Robustness Concerns for Hiding Information in Text\", Purdue\nUniversity, CERIAS Tech. Report 2004-13, 2004.","S.H. Low, N.F. Maxemchuk, J.T. Brassil, and L. O'Gorman, \"Document\nmarking and identification using both line and word shifting\",\nProceedings of the Fourteenth Annual Joint Conference of the IEEE\nComputer and Communications Societies (INFOCOM '95), April 1995.\n[10] K. Rabah, \"Steganography-The Art of Hiding Data\", Information\nTechnology Journal, vol. 3, Issue 3, pp. 245-269, 2004.\n[11] M. H. Shirali-Shahreza, and S. Shirali-Shahreza, \"A Robust Page\nSegmentation Method for Persian/Arabic Document\", WSEAS\nTransactions on Computers, vol. 4, Issue 11, Nov. 2005, pp. 1692-1698.\n[12] N. Provos and P. Honeyman, \"Hide and Seek: An Introduction to\nSteganography\", IEEE Security & Privacy, pp. 32-44, May/June 2003."]}

This paper presents a novel steganography methodology, namely Automatic Joke Generation Based Steganography Methodology Jokestega, that pursues textual jokes in order to hide messages. Basically, Jokestega methodology takes advantage of recent advances in Automatic Jokes Generation AJG techniques to automate the generation of textual steganographic cover. In a corpus of jokes, one may judge a number of documents to be the same joke although letters, locations, and other details are different. Generally, joke and puns could be retold with totally different vocabulary, while still retaining their identities. Therefore, Jokestega pursues the common variations among jokes to conceal data. Furthermore, when someone is joking, anything may be said which legitimises the use of joke-based steganography. This makes employing textual jokes very attractive as steganographic carrier for camouflaging data. It is worth noting that Jokestega follows Nostega paradigm, which implies that joke-cover is noiseless. The validation results demonstrate the effectiveness of Jokestega.

The frequent exchange of e-mails is widely popular and generates a high volume of traffic that allows communicating parties to establish a covert channel without a suspicious pattern rendering e-mails an attractive steganographic carrier to transmit hidden messages. This was the motive of developing e-mail-headers-based steganography methodology (Headstega). Headstega encodes a message then assigns it to steganographic carriers (e-mail-headers), e.g. recipient's e-mail addresses, names, subject fields, etc., in order to camouflage data by following Nostega paradigm. Thus, Headstega neither hides data in a noise (errors) nor produces noise while a message is concealed in the textual e-mail headers and the e-mail contents (the body of e-mails) are completely legitimate and do not conceal data. The presented implementation, validation and steganalysis of Headstega demonstrate: the robust capabilities of achieving the steganographic goal, the adequate room for concealing data and the superior bitrate to all contemporary text steganography approaches, which is roughly 3.38 7.67%.