A 2-tier Data Hiding Technique Using Exploiting Modification Direction Method and Huffman Coding


Published on

This paper proposes a 2-tier data hiding technique
that involves Exploiting Modification Direction (EMD) method
and Huffman Coding. Firstly, a secret message of an arbitrary
plain text is encrypted, compressed and transformed into a
stream of bits. Subsequently, the bits are converted into secret
digits by using the Huffman dictionary table. Secondly, a
cover image is segmented into groups of n pixels and each
group is embedded with one secret digit by modifying one
gray-scale value at most to hide the secret digit in (2n+1)-ary
notational system. The experimental results have shown that
both PSNR and payload of the proposed method are higher
than that of well-known methods namely, OPAP, EMD and
Opt EMD.

Published in: Technology, Art & Photos
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

A 2-tier Data Hiding Technique Using Exploiting Modification Direction Method and Huffman Coding

  1. 1. ACEEE Int. J. on Information Technology, Vol. 02, No. 02, April 2012 A 2-tier Data Hiding Technique Using Exploiting Modification Direction Method and Huffman Coding Ali m-Ahmad1a, Ghazali Bin Sulong1b, Mohd. Shafry B. Mohd. Rahim1c, Saparudin2 Department of Computer Graphic & Multimedia1, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Email:{ aa5555sobel@yahoo.com; bghazali@spaceutm.edu.my; cshafry@utm.my} Fakultas llmu Komputer2, Universitas Sriwijaya, Kampus Unsri-lndralaya, IO, Sumatera Selatan, Indonesia. Email: saparudin1204@yahoo.comAbstract—This paper proposes a 2-tier data hiding technique enemy is allowed to detect, intercept and modify messagesthat involves Exploiting Modification Direction (EMD) method without being able to violate certain security premisesand Huffman Coding. Firstly, a secret message of an arbitrary guaranteed by a cryptosystem, the aim of steganography isplain text is encrypted, compressed and transformed into a to hide secret information inside other harmless messages tostream of bits. Subsequently, the bits are converted into secret ensure nobody except the receiver can detect that there is adigits by using the Huffman dictionary table. Secondly, acover image is segmented into groups of n pixels and each second message present.group is embedded with one secret digit by modifying one The steganography system consists of two proceduresgray-scale value at most to hide the secret digit in (2n+1)-ary which are: the embedder and a detector. The embedder takesnotational system. The experimental results have shown that two inputs. One is the payload to embed the secret message,both PSNR and payload of the proposed method are higher and the other is the cover Work in which to embed thethan that of well-known methods namely, OPAP, EMD and payload. The output of the embedder is typically transmittedOpt EMD. or recorded. Later, that Work is presented as an input to the detector. Most detectors try to determine whether a payloadIndex Terms—data hiding, steganography, Exploiting is present, and if so, output the message encoded by it [1].Modification Direction (EMD) method, Huffman coding, data Thus stenography is used to protect secret information byprotection, cryptography. embedding secret messages in the host media (cover image), for instance: text, images, audio or video. I. INTRODUCTION The need for methods that provides efficient work on the II. BACKGROUND OF THE PROBLEMprotection of data and private property of individuals hasbecome very vital due to the huge growth of multimedia The basics of steganography are inaudibility, robustnessapplications on networks. It is therefore important to create and data rate (payload). The required trade-offs between themethods that provide security for the media from thieves and image quality and the payload, the steganography algorithm,hackers to prevent them from tampering and misrepresenting must be at an acceptable level. In steganography, it is verythe data. hard to embed a large amount of data and preserve the high Data protection consists of two techniques: image quality at the same time. Therefore, if it is required tocryptography and data hiding. Cryptography means the have more payloads, the image quality will be low. Conversely,provision of protection for data storage and data transfer a steganography algorithm is usually not efficient with highwhile using a secret key. Encryption is still a successful way payload embedding [3].to protect stored and transmitted data over a network. But, Steganography techniques are mostly divided in twowith the growing use of networks to send and receive data groups: spatial domain [8, 9, 11] and frequency domain [10,on the global information network, it has become very difficult 12, 13, 14]. The former embeds messages directly into theto maintain this data. Data hiding has two main approaches: image pixels. The Least Significant Bit (LSB) insertion methodsteganography and digital watermarking. is probably the most well-known image steganography technique. This method is easy to implement and has high These two approaches have many techniques [2], one of quality but, it is extremely vulnerable to attacks such as imagethem is LSB steganography which is defined as the practice manipulation (compression and low pass filtering).of undetectably altering a Work to embed a secret message. Contrary to the above method, frequency domainAlso, watermarking is defined as the practice of imperceptibly technique embeds messages in the frequency coefficients ofaltering a Work to embed a message about that Work [1]. images by using a frequency-oriented mechanism such asSteganography is also defined as the art and science of Discrete Cosine Transform (DCT) [17], Discrete Fouriercommunicating in a way which hides the existence of the Transform (DFT), Discrete Wavelet Transform (DWT) [17],communication. In contrast to Cryptography, where the and so on. Generally, embedding in the low frequency can© 2012 ACEEE 46DOI: 01.IJIT.02.02.34
  2. 2. ACEEE Int. J. on Information Technology, Vol. 02, No. 02, April 2012withstand many attacks but provide clear effect on the media.In contrast, embedding in the high frequency provide lessimpact on the quality of images but creates low robustnessto different attacks [16]. Where n indicates no. of pixels, g1, g2…, gn represent values Now the question is: How could the payload is increased of pixel within each group, s refers to index of the image, dwhile maintaining high image quality? indicates value of secret digit. If f = d no change is needed , if f # d and s lesser or equal to III. RELATED WORK n, g(s) is increase by 1.If f ‘“ d and s is greater than n, g(2n+1- ) is decrease by 1. The LSB is considered one of the most common methods s For example, let n=4, d=3 and the original pixel-group be [99,[6, 7, 8]. This approach deals directly with a cover-image, 110, 120, 130]. Then f = (99 * 1 + 110 * 2 + 120 * 3 + 130 * 4 )after hiding a secret image within it. In general, bit-mapped mod 9 = 2, s = (d – f ) mod 9 = 1 which is less than n. Thus, g(s)images are commonly used. Every image consists of a set of = g(1) + 1 = 100.pixels, and every pixel represents one colour. The values of a Second example: let d=9 and the same pixel-group isgray-scale image range from 0 to 255. If the value of the pixel employed. Then s = (d – f ) mod 9 = 7 which is bigger than n.is equal to 0, it signifies darkness, and when is equal to 255, thus, g(2n+1-s) = g(2) – 1 = 109.it indicates lightness. Therefore, a gray level image can be The embedded data can be extracted using the followingadjusted by adjusting these values. At least 8 bits are required extraction function of stego-pixel-group (4).to represent these values, and the binary system stores themfrom bits a1, a2… a8. The LSB substitution changes the last bit(a 1) to make imperceptible modification that cannot be Ref. [5] proposes the relationship between the value of n anddetected by the human vision system. For instance, if the amount of payload that minimizes cover image distortion.value of a pixel is 100, and we want to embed a 1, the pixel Here, the value of n is computed prior to embedding usingvalue becomes 101. Human vision cannot recognize this (5).difference. However, the LSB can easily embed secret datainto an image with an impalpable effect on the image. Thepixel values after embedding can be computed using (1). where Is means the total number of pixels of the cover image, x’i=xi - xi mod 2k +mi (1) n indicates the amount of pixels for each group and pWhere xi, and x’i refer to the pixel before and after embedding represents the payload to be embedded.respectively, k refers to the number of bits which going toembed and mi refers to the value of secret message. Other IV. PROPOSED METHODresearchers used Intermediate Significant Bit (ISB) planes The main aim of this method is to hide a large amount of[18, 19, 20, 21, 22, 23] to overcome the LSB drawbacks. Here, secret message in high secrecy and the same time preserveshigher bit-plane is selected to embed the secret messages. the cover image’s quality. Here, a new 2-tier data hidingThus, it reduces stego-image quality but increases technique is proposed that involves the EMD method androbustness. The higher the bit-plane is chosen the better the lossless Huffman coding. At first, the secret data isrobustness would be but the poorer image quality would encrypted, compressed and transformed into a stream of bits.become and vice versa [24]. Then the bits are converted into secret digits by using the In the Optimal Pixel Adjustment Process (OPAP) proposed Huffman dictionary table. Subsequently, the cover image isby [6], this algorithm is effectively reduced distortion. The segmented into groups of n pixels and each group isOPAP makes use of the last n bits to embed data and at the embedded with one secret digit by modifying one gray-scalesame time toggles the n+ 1 bit while comparing the toggle value at most to hide the secret digit in (2n+1)-ary notationalwith least distortion. For example, bit stream “0011” if theembedding is”00"; the result is either “0000” or “0100 “. It is system.obvious that changing “0011” to “0100” will cause less dis- Step1: Construct a table containing individual letters andtortion in comparison to “0000”. Although this technique their frequency numbers.may reduce distortion, it still weak when value of n is more Step2: Sort the letters in an ascending order according tothan two bits. Thus, the technique cannot handle higher im- their frequency numbers.age quality. Step 3: Add the first two frequency numbers and then re- Ref. [4], proposed the Exploiting Modification Direction arrange the table again.(EMD) to reduce distortion. EMD made use of n pixels as a Step 4: Repeat step 3 until a single frequency number isgroup to embed secret digits in a (2n+ 1)-ary notational achieved.system. During embedding stage, it requires to increase or Step 5: Construct Huffman tree by assigning each pair ofdecrease one from the value of a particular pixel within the branches with (0,1) for all branches of the tree.group using (2) and (3). Step 6: Rewrite the letters according to the above Huffman tree.© 2012 ACEEE 47DOI: 01.IJIT.02.02. 34
  3. 3. ACEEE Int. J. on Information Technology, Vol. 02, No. 02, April 2012Step 7: Construct the final table containing all the secret Step2: If f ‘“ d then s = d – f mod(2n+1) letters with their codes. Step3: If s < n then increase g(s) by 1Example, let the message be thisis Step4: if s > n then decrease g(2n+1-s) by 1 Step5: Repeat 1 to 4 until all the digits of the secret message are processed. Figure 2. Flow chart for embedding process C. Extraction of The Secret Codes The stego-image is received and (4) is applied to extract the codes. Then these codes are converted into binaries using Huffman dictionary and finally the original message of plain text is deciphered using Huffman decoding. Fig. 3 depicts the extraction method. Extraction algorithm Input: Stego-image Output: Secret data Step1: For i=1:n d=(sum(gi’ * i)) mod (2n+1) End Step2: Convert (d) to binary. Step3: Decode this binary number to original letter by using Huffman decoding. Step4: Repeat 1-3 until end of text. Figure 1. Huffman tree Figure 3.Flow chart for extraction processB. EMD Method This method hides each digit of the Huffman codes into V. EXPERIMENTAL RESULTSthe segmented cover image by increasing or decreasing byone gray-scale value at most for each group of n pixels [4]. An experiment is conducted using four standard gray-Here, the value of n is crucial to determine the right size of the scale images of 512x512 pixels as cover images shown in Fig.group. Therefore, (5) is used to calculate n which resulted in 4 namely, Baboon, Airplane, Lena and Tiffany. Furthermore,only minimum distortion for the cover image. Then (2) is an arbitrary plain text is used as the secret message. Toused to transform each n-pixel group into a reference value f. evaluate the quality of stego-image, the following peak signal-Embedding algorithm to-noise ratio (PSNR) is employed:Input: cover image (g1, g2, …..,gn), digits from Huffmanencoding d.Output: stego-image Where MSE refers to the Mean Square Error between twoStep1: For i=1:n pixels. f=(sum(gi * i)) mod (2n+1) end© 2012 ACEEE 48DOI: 01.IJIT.02.02. 34
  4. 4. ACEEE Int. J. on Information Technology, Vol. 02, No. 02, April 2012Where r and c refer to the number of rows and columnsrespectively for the cover image and stego-image. As shown in Table 1, for the purpose of a benchmark testbetween the proposed technique and various well-knownmethods namely, OPAP, EMD and Opt EMD, two differentsizes of payloads are used. Thus, the experimental results aredivided into two parts: The 1st. part utilizes full payload,whereas the 2nd. part uses half of it. The results have revealedthat our proposed method is superior than the rest of thetechniques in terms of both PSNR and payload viz. 60.31 dBand 36000 byte, respectively. The difference in terms of PSNRis quite significant between the proposed method and the 2ndbest method which is around 5 dB. As for the 2nd. part of theresults, although the payload is reduced to half, it is obviousthat the performance of the proposed method is maintainedin relations to both PSNR and payload. Therefore, theproposed method is capable of embedding large amount ofsecret messages without sacrificing image quality. TABLE I. THE RESULTS OF THE PROPOSED METHOD, OPAP, EMD AND OPT . EMD Figure 4. Test images (a)~(d) CONCLOSIONS In this paper, a 2-tier data hiding technique using Huffman coding and EMD method is proposed. The secret messages are encrypted, compressed and embedded in cover images with high secrecy without compromising the quality of the stego-images. Experimental results have revealed that the proposed method has successfully outweighed the well- known methods in both PSNR and payload. The experiments would be more meaningful if attacks were performed to measure the robustness; however this is beyond our scope of study at this juncture.© 2012 ACEEE 49DOI: 01.IJIT.02.02.34
  5. 5. ACEEE Int. J. on Information Technology, Vol. 02, No. 02, April 2012 REFERENCES [14] N. M. Nasrabadi, and R.A. King,” Image coding using vector quantization: a review,” IEEE Trans Commun, vol. 36(8), pp.[1] I. J. Cox, M. L. Miller, J. A. Bloom, J. Fridrich, and T. Kalker 957–971, 1988.“Digital and watermarking”, ELSEVIER, USA, 2nd ed., pp. 4-9, [15] A. Munteanu, et al., “Wavelet image compression—the2006. quadtree coding approach,” IEEE Trans Technol Biomed, vol. 3(3),[2] A. A. Zaidan, et al. “Novel multi-cover steganography using pp. 176–185, 1999.remote sensing image and general recursion neural cryptosystem”, [16] A. A. Shjul, and U. L. Kulkarni “secure skin tone basedInternational Journal of Physical Sciences, vol. 5(11), pp. 1776- steganography Using wavelet transform”, International Journal of1786, 2010. computer theory and Engineering, vol. 3, pp. 16-22, February,[3] C. Nedeljko, “Algorithm for Audio Watermarking and 2011.Steganography,” Acta Universitatis Ouluensis. Series C., 2004. [17] B. Chen, S. Latifi, and J. Kanai, “Edge enhancement of remote[4] X. Zhang, and S. Wang, “Efficient Steganographic Embedding image data in the DCT domain,” Image Vis Comput, vol. 17(12):by Exploiting Modification Direction”, IEEE Communications pp. 913–921, 1999.Letters, vol. 10(11), pp. 781-783, 2006. [18] A. M. Zeki, and A. A. Manaf, “A Novel Digital Watermarking[5] K. Y. Lin, W. Hong, J. Chen, T. S. Chen and W. C. Chiang, Technique Based on ISB (Intermediate Significant Bit)”,“Data hiding by Exploiting Modification Direction technique using International Journal of Information Technology, vol. 5, pp. 989-Optimal Pixel grouping”, International Conference on Education 996, 2009.Technology and Computer (ICETC), 2010, vol. 3, pp. 121-123. [19] Y. Dejun, Y. Rijing, Y. Yuhai, and X. Huijie, “Blind Digital[6] E. Adelson., “Digital signal encoding and decoding apparatus”, Image Watermarking Technique Based On Intermediate SignificantUS Patent, no. 4939515, 1990. Bit and Discrete Wavelet Transform”, Proc. of International[7] L. F. Turner, “Digital data security system”, Patent IPN Conference on Computational Intelligence and Software Engineering,WO 89/08915, 1989. CISE. IEEE Computer Soceity, 2009, vol. 15, pp. 1-4.[8] W. Bender, D. Gruhl, M. Morimoto, and A. Lu, “Techniques [20] S. M. Perumal, and V. V. Kumar, “A Wavelet based Digitalfor data hiding”, IBM Systems, vol. 35(3-4), pp. 313–336, 1996. Watermarking Method using Thresholds on Intermediate Bit[9] C.K. Chan, L.M. Cheng, “Hiding data in images by simple Values”, International Journal of Computer Applications, Vol. 15(3),LSB substitution”, Pattern Recognition, vol. 37(3), pp. 469–474, pp. 29-36, Feb 2011.2004. [21] A. Habes, “Information Hiding in BMP image Implementation,[10] H. Inoue, A. Miyazaki, and T. Katsura, “An Image Analysis and Evaluation”, Information Transmissions In ComputerWatermarking Method Based On the Wavelet Transform”, IEEE Networks, Tom. 6, No. 1, pp. 1-10, 2006.Communications Letters, vol. 1, pp. 296-300. Aug 2002. [22] B. A. Mehemed, T. E. A. El-Tobely, M.M. Fahmy, M. E. L.[11] C.C. Chang, J.Y. Hsiao, and C. S. Chan, “Finding optimal least Said Nasr, and M. H. A. El-Aziz, “Robust digital watermarkingsignificant- bit substitution in image hiding by dynamic based falling-off boundary in corners board-MSB-6 gray scaleprogramming strategy”, Pattern Recognition, vol. 36(7), pp. 1583– images,” International Journal of Computer Science and Network1593. 2003. Security, Vol. 9(8), pp. 227-240, August 2009.[12] W.C. Du, and W.J. Hsu, “Adaptive data hiding based on VQ [23] M. S. Emami, and G. B. Sulong, “A Statistical Method basedcompressed Images,” IEEE Proc. of Vis Image Signal Process, 2003, on L2Norm Technique for EISB Information Watermarkingvol. 150(4), pp. 233–238. Scheme”, Proc. of International Conference on Future Information[13] J. In, S. Hsiarani, and F. Kossentini, “On RD optimized Technology IPCSIT, Sep. 2011, Singapore, vol. 13, pp. 139-143.progressive image coding using JPEG,” IEEE Trans Image Process, [24] K. Rabah, “Steganography — The Art of Hiding Data,”vol. 8(11): pp. 1630–1638, 1999. Information Technology Journal, Vol. 3(3), pp. 245-269, 2004.© 2012 ACEEE 50DOI: 01.IJIT.02.02.34