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37 c 551 - reduced changes in the carrier of steganography algorithm

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37 c 551 - reduced changes in the carrier of steganography algorithm

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Steganography is the science that involves
communicating secret information in an appropriate
carrier so no ‎one apart from the sender and the recipient
even can recognize that there is hidden
information. ‎Steganography is the art of hiding
messages inside unsuspicious medium such as images,
videos, various ‎types‎ of‎ files…etc.‎ It's‎ a‎ method‎ to‎
establish a secure communication channel between two
parties. The ‎purpose of steganography is to hide the
existence of a message from an eavesdropper or third
parties. ‎Steganalysis is the branch of data processing
that seeks the identification of carrier vessels and
retrieval of ‎message hidden. In this paper we present
enhanced implementation for Steganography algorithm,
an ‎algorithm that we claim to be safe, built over DCT
(Discrete Cosine Transformation) frequency
domain ‎mutation[12], the algorithm uses error reductive
measurements such as pattern matching to obtain
a ‎reasonable a better image quality by reducing number
of changes that steganography algorithm made ‎during
the embedding process.‎

Steganography is the science that involves
communicating secret information in an appropriate
carrier so no ‎one apart from the sender and the recipient
even can recognize that there is hidden
information. ‎Steganography is the art of hiding
messages inside unsuspicious medium such as images,
videos, various ‎types‎ of‎ files…etc.‎ It's‎ a‎ method‎ to‎
establish a secure communication channel between two
parties. The ‎purpose of steganography is to hide the
existence of a message from an eavesdropper or third
parties. ‎Steganalysis is the branch of data processing
that seeks the identification of carrier vessels and
retrieval of ‎message hidden. In this paper we present
enhanced implementation for Steganography algorithm,
an ‎algorithm that we claim to be safe, built over DCT
(Discrete Cosine Transformation) frequency
domain ‎mutation[12], the algorithm uses error reductive
measurements such as pattern matching to obtain
a ‎reasonable a better image quality by reducing number
of changes that steganography algorithm made ‎during
the embedding process.‎

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37 c 551 - reduced changes in the carrier of steganography algorithm

  1. 1. Reduced Changes in the Carrier of Steganography Algorithm Mohammed Kharma 1 , Dr. Nedal Kafri 2 1: Al Quds University(Moh.kharma@gmail.com), 2: Al Quds University(nkafri@science.alquds.edu) ABSTRACT Steganography is the science that involves communicating secret information in an appropriate carrier so no ‎one apart from the sender and the recipient even can recognize that there is hidden information. ‎Steganography is the art of hiding messages inside unsuspicious medium such as images, videos, various ‎types‎ of‎ files…etc.‎ It's‎ a‎ method‎ to‎ establish a secure communication channel between two parties. The ‎purpose of steganography is to hide the existence of a message from an eavesdropper or third parties. ‎Steganalysis is the branch of data processing that seeks the identification of carrier vessels and retrieval of ‎message hidden. In this paper we present enhanced implementation for Steganography algorithm, an ‎algorithm that we claim to be safe, built over DCT (Discrete Cosine Transformation) frequency domain ‎mutation[12], the algorithm uses error reductive measurements such as pattern matching to obtain a ‎reasonable a better image quality by reducing number of changes that steganography algorithm made ‎during the embedding process.‎ I.INTRODUCTION Information security and hiding is a general term involves several sub disciplines and areas around a wide ‎spectrum of problems like embedding message into other contents. Information hiding concept denotes to ‎maintain the confidentiality of information or making the information cannot be detected. Many techniques’‎ have ‎been developed to hide the secret information in other container data to be viewed as an innocent.‎ Steganography is the art and science of writing hidden messages inside innocent looking containers in such a way ‎that no one apart from the sender and intended recipient even realizes the existence of a hidden message. ‎Steganography differs from cryptography in that the first makes the message unreadable while the second makes ‎it unseen. It is nevertheless possible to use both techniques to add security to the messages.‎ Steganography is a two-part word of Greek origin. “Stegano‎ graphy”‎ or‎ “cover/hidden/roof‎ writing”[19]. Its ancient ‎origins can be traced back to 440 BC When Demeratus sent a warning about a forthcoming attack to Greece by ‎writing it on a wooden panel and covering it in wax. A second classic example is that of Histiaeus, who had shaved the ‎head of his most trusted slave and tattooed a message on it. After his hair had grown the message was hidden. ‎The purpose was to instigate a revolt against the Persians. The third classic example is to hide the message ‎throughout the first character in the subject paragraph, so construction of the message can be achieved by taking ‎the first letter from every paragraph. Steganography used in electronic communication include steganographic ‎coding inside of a transport layer, such as an MP3 file, or a protocol, such as TCP and UDP. A wide variety of ‎steganography implementations in sound files, movies, exe files, videos and many other exiting file types. The ‎technique have had a lot of attention after the USA government had claimed the technique used by al-Quada ‎terrorists in there communication, Claims that were afterwards proven to be false [1].‎ In secure communication model and to illustrate steganography problem, participated parties in the ‎communication can be summarized as: Alice and Bob are trying to communicate a secure message. However, all ‎there communications are being checked and filtered by third party who want to know the secrete message they ‎want to communicate, to achieve the non delectability in Alice and Bob communication so they use another ‎container called cover-object to embed the secrete message into it[1].‎ Discrete cosine transform (DCT) is the most well- known transform coding techniques for converting a signal into ‎elementary frequency components used to implement lossy image compression (such as JPEG format) to ‎transform the image from the spatial domain to the frequency domain. DCT separates the image into three ‎different frequency components: high, medium and low where the image is segmented into non-overlap 8 pixels x ‎‎8 pixels blocks. The DCT is computed for each block starting from left corner to right corner in top-bottom order ‎‎[13].‎ The rest of this paper is organized as follows: Section 2 gives a background regarding the main schemes of ‎steganography; spatial domain and frequency domain, and their evaluation techniques. While Section 3 introduces ‎the proposed steganography
  2. 2. algorithm Section 4 presents and discusses the obtained experimental results. ‎Finally, Section 5 concludes the paper.‎ II. BACKGROUND Steganography is one of main aspects of secure communication channels and widely used techniques that ‎manipulate information in order to hide their existence rather than encrypting it using cryptography methods. Early ‎steganographic algorithms considered only human abilities to spot Irregularities as the only detection technique. ‎Those algorithms implementations are widely used in Image Steganography and relies on the fact that computer ‎images normally have quite a bit of redundant data and that changing the contents of those data (as pixels or color ‎plate elements) could make us enough space to embed a considerably large message (50% data rate with BPCS ‎Steganography [5]), where BPCS stands for Bit-Plane Complexity Segmentation [5]. Not all early Steganographic ‎algorithms had huge data rate of the BPCS, but most of all algorithms perform their data embedding in very ‎unique manner that create some unique irregularities to distinguish each algorithm from others.‎ Classification of Image Steganographic techniques can be done based on which domain has to be used during ‎embedding the secure message into two groups: Spatial/image Domain and Frequency/Transform Domain. ‎Spatial domain techniques embed the secure message stream in the image pixels directly, while in frequency ‎domain, images are first transformed from spatial to the frequency domain and then the message steam is ‎embedded in the transformed form of the image [2].‎ A. ‎Spatial Domain steganography‎ First generation of steganography, embedding process uses the spatial domain of the image to embeds the ‎message data in a sequential order in the Least Significant Bit of image pixels [16][17]. One of the disadvantages ‎of this technique is the weak immunity against compression where there is a fear for damage of the message that ‎may have sensitive information [11], in addition to the poor immunity against visual attaches and the simplicity to ‎extract the embedded message by walkthrough around image pixels. To decrease the ability on extracting the ‎embedded message, A Pseudo- Random Number Generator (PRNG) works based on input parameter used as a ‎secret key in order to generate randomized ordered locations to be used for hiding the message bits instead of the ‎sequential order of message embedding [18]. ‎ Moreover, in 2003 Rios and Puech[13] introduce a new approach depending on imperceptible of human eye to a ‎small variation in the channel color value of a colored image [14] and this approach called SSB-4 steganography ‎approach [15]. The main concept in SSB- 4 is to embed the secure message into the 4th bit of the original image ‎pixel, and to minimize the value difference between the modified pixel value that have the secret message bit and ‎the original pixel value, modify the 1st, 2nd, 3rd and/or 5th pixel values to align modified pixel value to the original ‎value.‎ Unfortunately, LSB didn't stand forever as new evolving branch of data analysis was born "Steganalysis". The first ‎released results of steganalysis was a paper by Andreas Westfeld and Andreas Gtzmann[6]. Gtzmann and ‎Westfeld made clear how to attack a number of very famous image based steganography algorithms both visually ‎by the use of some image techniques and naked eye, or by automated statistical algorithms. The two attacks ‎introduced were the filter attack and the PoV statistical attack [6]. Both attacks were designed to address the ‎Steganographic systems of spatial domain embedding.‎ B. Frequency Domain Steganography Unlike spatial domain, the data embed into image pixels directly, in transform domain, image representation is ‎transformed to frequency domain before start hiding message data into image [6][10]. The main factors to determine ‎image payload to embed the message are the number of pixels and the color depth with taking into consideration ‎preserving the invisibility of image data changes during embedding process [3]. it’s‎worth‎to‎mention‎the‎‎observation‎of‎Wang’s‎in‎there‎ paper ‎[21] where they find ‎data embedding in the frequency domain, cause the ‎hidden data ‎spread across the entire image in more robust ‎areas, and gives a better immunity against signal ‎processing[21].‎ Discrete cosine transform (DCT) is the most well- known transform coding techniques that is widely used in ‎converting a signal into elementary frequency components used to implement lossy image compression (such as ‎JPEG format) to transform the image from the spatial domain to the frequency domain. DCT separates the image ‎into three different frequency components: high, medium and low where the image is segmented into non-overlap ‎‎8 pixels x 8 pixels blocks. The DCT is computed for each block starting from left corner to right corner in top-bottom ‎order [13]. In this paper, we consider the DCT as an example of the frequency transformation technique that can be ‎used.‎ To enhance the security of embedding using LSB a ‎Pseudo-Random Number Generator (PRNG) and a secret ‎key have been used to specify the order of access to the ‎embedded information ‎‫‏‬ as introduced by F5 ‫‏‬Algorithm according [17] ‫‏‬ where a user password in included algorithm as input to be used for PRNG. The
  3. 3. embedding process uses the user password to get the seed for PRNG and then to generating a random walk through the DCT coefficients of the cover image Another approach uses the randomization in embedding the secret message introduced by Bani Younes et al[4] who used the encrypted image to send the secret information through it. The basic of this method is to mixed the generate number of horizontal and vertical blocks at the sender side with the encrypted image before sending it to the intended party. In this method, the authors select randomly based on a user input key a number of bytes to replace there LSB with the binary representation of the hidden data within the encrypted image and by this way the hidden information will spread among the encrypted image data randomly based on the generated locations by the user secret key. Following the attacks by Gtzmann and Westfeld a new Steganographic system emerged to use the transformed ‎‎image to frequency domain using discrete cosine transformation algorithm as carrier for embedded data instead ‎‎of embedding the message directly to the pixels in spatial domain [6][10].Steganographic methods that use ‎embedding in frequency domain have the immunity ‎against PoV and filter attack, but had a lower data rate. ‎OutGuess and F5 can be ‎considered as an example for this implementation [7, 8] where F5 use the DCT ‎‎coefficients to embed to embed the secure message inside them with excluding some ‎coefficients values such as ‎zero AC coefficients. The concept of F5 algorithm is instead of replacing the LSBs of quantized DCT coefficients ‎with the message bits, the absolute value of the randomly selected coefficient is decreased by one. F5 authors ‎claimed that this type of embedding cannot be detected using their ᵡ2 statistical attack [18].‎ In order to increase complexity against steganalysis, instead LSB, hidden data can be embedded in the Bit-4 of the DCT coefficient [10]. C. Stego Image Quality Measure The idea behind steganography is to preserve the secrecy of embedded message by hiding every existence of the ‎concealed message. As the obscurity is the art of Steganography, the human perception immunity of the stego-‎image against detection is the most important test. The most used tests are the Subjective in addition to the ‎mathematical tests, Peak signal-to-noise ratio (PSNR calculated in dB (decibel) ‎) is a commonly used ‎measurement of the picture degradation, which is calculated as the error between the original image and stego-‎image[11].‎ ITU (International Telecommunication Union) defined a set of rules and recommendation of subjective tests. The ‎subjective tests are carried out by people who look for visual differences between the original and the stego- ‎image to trying to. If the percentage of success greater than 50%, it can be concluded that the message is invisible ‎‎[14][2].‎ Peak Signal to Noise Ratio (PSNR) is a technical approach usually used to evaluate the real quality of stego image ‎‎[13][4]. This technique is an engineering term for the ratio between the maximum possible power of a signal and ‎the power of corrupting noise that affects the fidelity of its representation. The PSNR is most commonly used to ‎measure the quality of reconstruction in an image; by comparing the stego image with the original image. III. THE PROPOSED METHOD Our contribution will be introducing a Pattern Matching algorithm to be used during Steganography embedding ‎process, this will enable finding reasonable match between the cover image and the message binary stream ‎which will result in minimizing distortion and modification that can be caused by modifying the image data to hide ‎the secrete message data by Steganography algorithm. We are proposing an improved implementation for ‎steganography algorithm on top of DCT (Discrete Cosine Transformation) frequency domain where the ‎implementation will includes implementation for Pattern Matching algorithm, where this algorithm will try to ‎generate random message steam data locations based on random keys generator that keep working until reach ‎certain threshold value or maximum number of trials, below is the pseudo-code for the proposed algorithm. Thus ‎will increase the ratio for of the data to be embedded with taking advantages from the face of extremely low ‎embedding rate prevents all known statistical attacks[9].‎ In Figure 1‎‫‏‬, the general description to ‫‏‬depict‫‏‬ the ‫‏‬process of the proposed method. The first step is to partitioning the ‎image into non-overlapped 8x8 ‎blocks, to transfer the image blocks to frequency domain, we apply DCT on each ‎block. When the corresponding DCT blocks computed for the original image blocks, the pattern matching algorithm ‎tries to find the most reasonable match between DCT LSB values and the message bits that we want to hide. Once ‎the reasonable ‎match has been found, message data embedded into least significant bit ‫‏‬of the ‎ nonzero DCT ‎coefficients. Lastly we apply the IDCT on each block to producing the stego-image which ‎can be transferred to the ‎intended recipient. Our approach is illustrated in details in the ‎following five steps (algorithm):‎
  4. 4. Figure 1. The process of the proposed method Step 1: Applying 2D DCT On Image Pixels In this step, the image is partitioned into non- overlapped 8x8 blocks. Thus each block F(x,y) consists of 64 pixels ‎values. ‎In case the image is ‫‏‬‎8 bits depth monochromic‎, then F(x,y) consists of the whole pixels' values. And if the ‎image is RGB 24-bits depth colored, then each block F(x,y) is constructed from only the least significant bytes (i.e., ‎Blue color channel/contribution) of the successive pixels. This is because the Blue channel is the most ‎imperceptible to human eye. Then we calculate the 64 DCT coefficients S(v,u) of each grid F(x,y) of the image ‎using DCT equation[20].‎ Step 2: Finding The Reasonable Pattern Match Between DCT Blocks And Message Data Herein the image is transferred into frequency domain/DCT blocks, the pattern matching algorithm is started to ‎enable finding accepted matching between the cover image and the message binary ‎stream which will result in ‎minimizing distortion and modification that can be caused ‎by modifying the image data to hide the secrete ‎message data. Our pattern matching algorithm is illustrated in details in the ‎following code:‎ 1.ReasonablePatternMatch() : INTEGER /*Return the number ‎that generate the reasonable match between ‎the ‎random locations in the image and ‎the ‎message*/‎ 2.BEGIN 3.INTEGER BFK= 0; /*choose initializing seed to generate rando locations*/‎ 4.INTEGER bestGainedKey = BFK;/*suppose‎that‎it’s‎ the ‎best generated key*/‎ 5.INTEGER bestError = 99999999; /*Initial value where ‎this vaiable will be update if the tested generated key ‎produce less ‎‎location mismatch between the generated ‎locations in the stegoimage and the message*/‎ 6.WHILE TRUE LOOP 6.1.INCREMENT(BFK); 6.2.order = getOrder(BFK);/*Get random locations ‎based on BFK value*/‎ ‎ 6.3.FOR each bit in msg data LOOP /*loop ‎to compare the msg data and image data */‎ 6.3.1.IF msg bit not equal LSB LSB in image[order[i]] THEN /*(image[order[i]]>>1<<1 | message[i] ‎‎!= image[order[i]]) compare the message bit with the ‎most left image ‎‎bit, if they are not similier then this will ‎count as error since when we embed the message in the ‎image then such ‎‎image data will be chaged according ti ‎the message data*/‎ ‎ 6.3.1.1. INCREMENT(error); /*mismatch counter increment*/‎ ‎ 6.3.2.END IF ‎ 6.3.3.IF error less than bestError THEN/*if we get minimum ‎error rate compared with prev. error rate*/‎ ‎ 6.3.3.1.bestGainedKey = BFK; /*update the best generated key*/‎ ‎ 6.3.3.2.bestError = error; /*update the minimum ‎error rate we get*/‎ ‎ ‎6.3.4.END IF 6.4.END LOOP 6.5.IF bestError less than MaxError THEN /* if we reach the ‎acceptable error rate*/‎ 6.5.1Return bestGainedKey; /* return the key to be ‎used*/‎ 6.6.END IF 7.END WHILE 8.RETURN bestGainedKey; /* return the key to be ‎used*/‎ 9.END Reasonable Pattern Matching Algorithm(RPMA) In the above Reasonable Pattern Matching Algorithm (PRM) uses a supplementary algorithm called in step 6.2 named getOrder to return a list of random locations that will be used ‎to find the locations in the stego-image ‎that will be used to ‎hide the message data inside it. Step 3: Embedding Message Bits‎ At this stage, to start message bits embedding, the generated random locations found in step 2 will be used to ‎embed the message into the DCT coefficients based on the generated sequence. Message will be embedded into ‎the least significant bit of the DCT coefficients with exclude the zero DCT values.‎ Step 4: Construct The Stego Image‎ Lastly, the stego-image is constructed by replacing each original image block F(x,y) by the modified stego block ‎F'(x,y) where the stego-image contains the image data in addition to the hidden message data.‎
  5. 5. Unlike the other techniques such as F5, our approach will have no significant effect on the DCT blocks of the ‎stego-image F'(x,y) generated in step 3, because in step 2, the pattern matching algorithm will try to find the ‎appropriate locations inside the cover image to hide the secrete message taking into consideration the minimum ‎number of stego-image DCT coefficient changes. ‎ IV. EXPERIMENTAL RESULTS Our contribution will be introducing Pattern Matching algorithm to be used during ‎steganography embedding ‎process, this will enable finding a better matching between the ‎cover image and the message binary stream which ‎will result in minimizing distortion ‎and modification that can be caused by modifying the image data to hide the ‎secrete ‎message data. To test the actual validity of our Pattern Matching algorithm, we ‎implemented an algorithm ‎to measure the probability to find a pseudo random locations ‎in the cover image we can hide the secrete message ‎in them with minimum changing in ‎original image data.‎ To validate our algorithm and results, we have selected four different images with same ‎dimension size to be used ‎as the container that the secure message will be embedded ‎inside it. And also we have selected two secrete text ‎messages with different sizes 10 ‎and 20 bytes respectively, to test our approach we deal with the text message as ‎a binary ‎steam and start our experiment by first image by reading the Original Image, transfer it ‎to DCT image ‎data, generate random locations in the DCT image data based on random ‎number generator to be used as a ‎location for hiding the secrete message, after that find ‎the middle frequency values[22] from these locations, ‎compare them (the least ‎significant bit value of these DCT data)with the binary stream of secure message data ‎‎and recording the match and mismatch between both image and message data. We have tested ‎the similarity ratio ‎improvement between the message and the random selected image ‎data by making the algorithm to be run on ‎five stages: first stage run the algorithm for ‎‎50 times to find the similarity and then run it again up to 200 ‎times, 400, 800 and 1600 ‎time to measure the effectiveness of finding best matches between the secrete message ‎‎data and image, following ‎ finger‎2‎and‎3‎show our‎ experiments results for 10 and 20 bytes message size respectively. Experimental statistics on the proposed pattern matching algorithm 10 bytes message Image Name Dimension Pattern matching algorithm Iteration Worst generated key Similarity ratio Worst key Best generated key Similarit y ratio Best key Flowers.jpeg 400 x 300 50 1932935652 21% -946158187 41% Flowers.jpeg 400 x 300 200 305530653 18% -1816578671 43% Flowers.jpeg 400 x 300 400 1917256675 16% -1494646998 45% Flowers.jpeg 400 x 300 800 -1584762429 13% 1708490964 46% Flowers.jpeg 400 x 300 1600 -1471890995 10% -488980360 47% People (Lena) .jpeg 400 x 300 50 -1582118205 20% 1852959459 43.75% People(Lena) .jpeg 400 x 300 200 -1582118205 20% 1852959459 43.75% People(Lena) .jpeg 400 x 300 400 -1087887718 16.25% 1852959459 43.75% People(Lena) .jpeg 400 x 300 800 -1087887718 16.25% -1087887718 46.25% People(Lena) .jpeg 400 x 300 1600 2117760478 16.25% -1087887718 46.25% Bridge(Landon).jpeg 400 x 300 50 -1912515890 36% -1111958610 53% Bridge(Landon).jpeg 400 x 300 200 306857764 35% -1111958610 53% Bridge(Landon).jpeg 400 x 300 400 306857764 35% -1111958610 53% Bridge(Landon).jpeg 400 x 300 800 306857764 35% -1111958610 53% Bridge(Landon).jpeg 400 x 300 1600 306857764 35% -1111958610 53% Mountains.jpeg 400 x 300 50 -605538897 20% -933251167 41% Mountains.jpeg 400 x 300 200 -605538897 20% 125526107 45% Mountains.jpeg 400 x 300 400 965414341 18% 125526107 45% Mountains.jpeg 400 x 300 800 965414341 18% -883571161 46%
  6. 6. Mountains.jpeg 400 x 300 1600 445598606 13% -883571161 46% Figure 2. Experiments results for 10 bytes message size 20 bytes message Image Name Dimension Pattern matching algorithm Iteration Worst generated key Similarity ratio Worst key Best generated key Similarit y ratio Flowers.jpeg 400 x 300 50 2002860108 24% 1644278906 42% Flowers.jpeg 400 x 300 200 -655012674 23% 1644278906 47% Flowers.jpeg 400 x 300 400 281662087 21% 1644278906 44% Flowers.jpeg 400 x 300 800 281662087 21% 1644278906 47% Flowers.jpeg 400 x 300 1600 1368526818 19% 1644278906 47% People (Lena) .jpeg 400 x 300 50 387420073 20% -648114300 40% People(Lena) .jpeg 400 x 300 200 387420073 20% -648114300 40% People(Lena) .jpeg 400 x 300 400 387420073 20% -648114300 40% People(Lena) .jpeg 400 x 300 800 387420073 20% 1079306710 41% People(Lena) .jpeg 400 x 300 1600 387420073 20% 1879938708 49% Bridge(Landon).jpeg 400 x 300 50 397203127 43% 1891607262 53% Bridge(Landon).jpeg 400 x 300 200 1963787566 42% -69194182 54% Bridge(Landon).jpeg 400 x 300 400 1963787566 42% -69194182 54% Bridge(Landon).jpeg 400 x 300 800 -1425495006 41% -69194182 54% Bridge(Landon).jpeg 400 x 300 1600 -1425495006 41% 1333860696 56% Mountains.jpeg 400 x 300 50 -1437932735 23% 926323778 38% Mountains.jpeg 400 x 300 200 1483788745 19% -736319659 42% Mountains.jpeg 400 x 300 400 1483788745 19% -736319659 42% Mountains.jpeg 400 x 300 800 1483788745 19% -736319659 42% Mountains.jpeg 400 x 300 1600 1483788745 19% 1087559179 43% Figure 3. Experiments results for 20 bytes message size According to our observation and stated results in tables above, we can say that using pattern ‎matching algorithm ‎will be reasonable and fructiferous idea as this algorithm will be ‎used to reduce the needed number of changes to ‎hide a message inside cover image ‎taking into account the length of the message where the length of the ‎message to be ‎hidden are In direct proportion with computations needed to find best matching ‎positions in the ‎cover image. Below ‎is the sample images we have used in experiments‎:‎ Figure 4. Images that have been used in previous experiments(experiments results in figure 2 and 3) As a visual example, the bellow figure 5 A is the source image that used by our Steganographic algorithm to produce finger B the stego-image. It is clear that the stego-image are imperceptible‎‫‏‬and also has a very good PSNR which is equal to ‎54.650‎‎8‎.
  7. 7. Figure 5.A Original Image Figure 5.B Stego-image V. CONCLUSION ‎In this paper we present an enhanced algorithm for Steganography, this algorithm that we claim ‎to be safe, built ‎over DCT (Discrete Cosine Transformation) frequency domain mutation, the algorithm ‎uses pattern matching ‎‎approach ‫‏‬to obtain a reasonable a better image quality ‎by reducing number of changes that steganography ‎algorithm made during the embedding process in carrier which will lead to finding less differences between the stego-‎image and original one.‎ According to our observation and obtained results, pattern ‎‎matching algorithm ‎will be a ‎reasonable and fructiferous idea as the algorithm will reduce the ‎needed number of changes to ‎hide a message ‎inside cover image ‎which is the very important to reduce image distortion and preserve a good image quality ‎compared to the original cover image. ‎ REFERENCES [1] Wikipedia contributors. Steganography. Wikipedia, the Free Encyclopedia; 2011 ‎‎.‎‎ [2] J.‎ Lenti,‎ “Steganographic‎ methods,”‎ periodic polytechnic ser.el.eng 44, No.3-4 , pp 249-258, Jun 2002.‎ [3] C.‎ C.‎ Chang,‎ T.‎ S.‎ Chen,‎ and‎ L.‎ Z.‎ Chung,‎ “A‎ steganographic method based upon JPEG and ‎quantization ‎table‎ modification”‎ Information Sciences, Volume 141, Issues 1-2, Pages 123- 138, ‎March 2002.‎ [4] M.‎ A.‎ Bani‎ Younes,‎ A.‎ Jantan,‎ “A‎ New‎ Steganography Approach for Image Encryption Exchange by Using the Least Significant Bit Insertion”,‎ IJCSNS,‎ International Journal of Computer Science and Network Security, Vol. 8 No. 6, June 2008. [5] Eiji Kawaguchi and Richard‎O.‎Eason,‎“Principle‎ and applications of BPCS-‎Steganography”,‎ University of ‎Maine ‎and Kyushu Institute of Technology, 2000‎ [6] Niels‎ Provos,‎ “OutGuess-Universal Steganography”, outguess.org, August 2001 [7] A. Westfeld, “F5 Steganographic Algorithm: High Capacity Despite Better ‎Steganalysis, Proc”. 4th Intil Work ‎shop Information Hiding, Springer- Verlag, pp 289-302, ‎‎2001.‎ [8] N. Provos and P. Honeyman, “Detecting Steganographic Content on the Internet”, ‎Proc. 2002 Network and Distributed System Security Symp.,Internet Soc, 2002.‎ [9] J. Fridrich, M. Goljan, R. Du, "Reliable Detection of LSB Steganography in Color ‎and Grayscale Images", workshop on Multimedia and security: new challenges, Pages 27 – 30,2001 [10] Kafri‎ N.‎ and‎ Sulaiman‎ H.,‎ “Bit-4 of Frequency Domain-DCT Steganography ‎Technique”,‎ Networked Digital technology NDT2009, VSB Technical University, ‎Czech Republic, 29-31, July 2009.‎ [11] N.‎Ahmed,‎T.‎Natarajan,‎and‎K.‎R.‎Rao,‎“Discrete‎ cosine‎transform,”‎IEEE nuns. ‎Compur., vol. C-23, pp. 90-94, Jan. 1974.‎ [12] Abbas Cheddad et al,” Digital image steganography: Survey and analysis of ‎current methods, signal Processing 90 ,727–752‎,2010 [13] J.‎ Rodrigues,‎ J.‎ Rios,‎ and‎ W.‎ Puech‎ “SSB-4 System‎ of‎ Steganography‎ using‎ bit‎ 4”,‎ In International Workshop ‎on Image Analysis for Multimedia WIAMIS, (Montreux),May 2005.‎ [14] International Telecommunication Union, “Information‎ Technology- Digital Compression and Coding of ‎Continuous-Tone Still Images - Requirements and Specifications Recommendation T.81”,‎ITU, Sept 1992‎
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