A Review On Modern Secure Mosaic Video Generation For Secure Video Transmission

A Review On Modern Secure Mosaic Video Generation For Secure Video Transmission Mr.Swapnil Patil, Prof.A.A Deshmukh DEPARTMENT OF ENTC ENGINEERING G. H. Raisoni Institute of Engineering and Technology Abstract A new method of combining art image generation and hiding a secret image into this cubism like image to enhance the camouflage effect for various information-hiding applications is proposed. First, a new type of computer art, called linebased Cubism-like image, which keeps a characteristic of the cubism art created by extract prominent lines and regions. For creating the mosaic video we need two video named as source and target video files. The first process is picking the target and the source audio for mosaic creation. Picking of target is similar to that of the selected source video but not the same. Then converting the lager source video into several small video for secret purpose. The splitting is used to placed the source video into target video. after that using the separate algorithm to find the most similar part of target image for placing the source tile video. This would be done for all the split source video. Then we are composing these video in a mosaic form for hiding the secret information. The mosaic video is same as that of the target video but this contain the tile source audio not hear. The output video will send to the destination, in destination re-mosaic process is held based on the some algorithm. The tile images are retrieving first for constructing the source image as send by the sender. After that the tile images are combined to create a original Video send by the sender. Keyword: Cubism.LSB,Mosaic etc I. INTRODUCTION Mosaic is a type of artwork created by composing small pieces of materials, such as stone, glass, tile, etc. Invented in ancient time, they are still used in many applications today. Creation of mosaic images by computer [1] is a new research direction in recent years.. A good survey under a unified framework can be found in Battiato et al. [2] in which a taxonomy of mosaic images into four types is proposed, including crystallization mosaic, ancient mosaic, photo-mosaic, and puzzle image mosaic. The first two types are obtained from decomposing a source image into tiles (with different colors, sizes, and rotations) and reconstructing the image by properly painting the tiles, and so they both may be called tile mosaics. The other two types of mosaics are obtained by fitting images from a database to cover an assigned source image, and both may be called multi-picture mosaics. A new type of art image, called secret-fragment-visible mosaic image, which contains small fragments of a given source image is proposed in this study. Observing such a type of mosaic image, one can see all the fragments of the source image, but the fragments are so tiny in size and so random in position that the observer cannot figure out what the source image looks like. Therefore, the source image may be said to be secretly embedded in the resulting mosaic image, though the fragment pieces are all visible to the observer. And this is the reason why the resulting mosaic image is named secret-fragment-visible. which is the result of random rearrangement of the fragments of a secret image in disguise of another image called target image, creating exactly an effect of image steganography. The difficulty of hiding a huge volume of image data behind a cover image is solved automatically by this type of mosaic image. This is a new technique of information hiding, not found in the literature so far. II Literature Survey In the proposed system initially the source image is converted into Cubism-like-art image by extracting prominent lines and regions. Yi-Zhe Song, Paul L. Rosin, Peter M. Hall and John Collomosse [3] proposed a method to simple shapes (e.g. circles, triangles, squares, super ellipses and so on) are optimally fitted to each region within a segmented photograph. Stipple Placement using Distance in a Weighted Graph is proposed by David Mould [4] provides extra emphasis to image features, especially edges. Regarding lossless data hiding, several techniques have been proposed. Xiaomei Quan and Hongbin Zhang proposed "Lossless Data Hiding Scheme Based On Lsb Matching [4] deals data hiding based on bit change. A lossless data hiding method based on histogram shifting and encryption is proposed by Nutan Palshikar and Prof. Sanjay Jadhav, and C. Liu in Lossless Data Hiding using Histogram Modification and Hash Encryption Scheme [5]. A novel scheme for separable reversible data hiding in encrypted images developed by Nutan Palshikar, Prof. Sanjay Jadhav in Separable Reversible Data Hiding in Encrypted Image [6]. A new secure image transmission technique which automatically transforms a given large-volume secret image into a socalled secret-fragment visible mosaic image of the same size [7]. A pioneering work done by Wei-Jen Wang, Cheng-Ta Huang, and Shiuh-Jeng Wang, proposed a state-of-the-art review and comparison of the different existing data-hiding methods for VQ-based images in "VQ Applications in Steganographic Data Hiding Upon Multimedia Images"[7] and _Real-Time Audio Watermarking Based on Characteristics of PCM in Digital Instrument [8] is a work done by Kotaro Yamamoto and Munetoshi Iwakiri. A lot of research carried out in data hiding inside compressed video in "Data Hiding in Motion Vectors of Compressed Video Based on Their Associated Prediction Error" [9] and "Robust Video Data Hiding Volume 4, Issue 2, March April 2015 Page 222

Using Forbidden Zone Data Hiding and Selective Embedding [10]. III RELATED WORK Existing system In traditional methods secret text can be hidden into image which is called as Steganography. In this method only text data can be encrypted but not image. Secret images can be hidden using water marking principles. Water marking is very simple process and it is weak that anyone can decrypt easily. Mosaic image technique is one of the efficient techniques to hide the secret images. This methodology needs another image which is said to be cover image. Creating mosaic image is also a art of computer. Many methods have been proposed to create different types of mosaic images by computer. Crystallization mosaic, ancient mosaic, photo-mosaic, and puzzle image mosaic are four types of mosaic images. An image is fragmented into small tiles. Then these tiles are randomly embedded onto a cover image. For encryption embedding process should be performed in some order. Secret key is used for embedding the small tiles of secret image onto cover image. LSB (least significant bit) replacement scheme is a technique mainly used for embedding process. LSB technique reduces or avoids the blur effect of encrypted mosaic image. Existence Architecture Fig 1 Existence Architecture Phase 1 creation of a secret-fragment-visible mosaic image using the tile images of a secret image and the selected similar target image as input. Phase 2 recovery of the secret image from the created secret-fragment-visible mosaic image. The first phase includes three stages of operations. Stage 1.1 searching a target image most similar to the secret image. Stage 1.2 fitting the tile images in the secret image into the blocks of the target image. Stage 1.3 create a blank image to create a mosaic Image. Stage 1.4 embedding the tile-image fitting information into the mosaic image for later secret image recovery. And the second phase includes two stages of operations: Stage 2.1 retrieving the previously-embedded tile image fitting information from the mosaic image. Stage 2.2 reconstructing the secret image from the mosaic image using the retrieved information. Proposed methodology has been divided into 2 phases. Fig 2 Mosaic Image Creation Mosaic Image Creation Fig 1 shows In this first phase, Shamir secret sharing algorithm is used by which a secret is divided into parts, giving each participants its own unique part, some of the parts or all of them are needed in order to reconstruct the secret counting on all participants to combine together, the secret might be impractical and therefore sometimes the threshold scheme is used. Now fitting the tile images of the secret image into the target blocks of a preselected target image. After this transforming the color characteristic of each tile image in the secret image to become that of the corresponding target block in the target image and rotating each tile image into a direction with the minimum RMSE value with respect to its corresponding target block. After the rotation embedding relevant information into the created mosaic image for future recovery of the secret image. In this way we get the output secret fragment visible mosaic image. Secret Image Recovery In this second phase, extracting the embedded information for secret image recovery from the mosaic image, and recovering the secret image using the extracted information by secret image recovery algorithm. In this phase result will be calculated and optimize if required result is in the form of delay and accuracy Fig 1 Main Module at sender and Receiver Volume 4, Issue 2, March April 2015 Page 223

There are two major stages in the proposed Image Stenographic Technique 1 Sender Side 2 Receiver Side At sender side a new type of computer art, called linebased Cubism-like image, which keeps a characteristic of the Cubism art created by extract prominent lines and regions. Then the cubism like image is divided into target tiles and the secret is also divided into secret tiles of same size as target. A mapping sequence is created based on secret-target tile similarity and the secret image is embedded into the target using that mapping sequence. To enhance security a secret key is shared between sender and receiver. The secret key will generate a random permutation that is used to permuting the mapping sequence. That mapping information is also embedded into the Cubism Image. Finally a secret-embeddedmosaic-image is created as steno image and that is sent to the receiver. At the receiver side when the receiver gets the output image, using the common secret key, he retrieves the mapping sequence and using that mapping sequence he will extract the secret image from the cubism image. Basic Idea of Secret Fragment Visible Mosaic Image The formation of mosaic image which visually approximates the target image using the tiles or blocks from the secret image and the recovery of secret image from the encrypted mosaic image has the following basic steps 1. Construction of a color image database for use in selecting similar target images for given secret image 2. Creation of a secret-fragment-visible mosaic image using the tile images of a secret image and the selected similar target image as input 3. Recovery of the secret image from the created secret fragment- visible mosaic image The first step includes selection of a wide variety of images and also calculation of some image similarity measures and histogram. The second step includes 1. Searching the database for a target image the most similar to the secret image 2. Fitting the tile images in the secret image into the blocks of the target image to create a mosaic image 3. Embedding the tile-image fitting information into the mosaic image for later secret image recovery The third step corresponds to the decryption. It has the following steps 1. Retrieving the previously-embedded tile-image fitting information from the mosaic image 2. Reconstructing the secret image from the mosaic image using the retrieved information Fig 3 Proposed Work The existing work proposes a methodology of generating image mosaics. Our image mosaic generating system divides an input image into many tiles; and then for each tile, it fetches the image with the most similar content from an image database and replaces the tile with the image. Steps Phase 1 creation of a secret-fragment-visible mosaic audio using the tile audio of a secret audio and the selected similar target audio as input. Phase 2 recovery of the secret image from the created secret-fragment-visible mosaic audio. The first phase includes three stages of operations. Stage 1.1 searching a target audio most similar to the secret audio. Stage 1.2 fitting the tile audio in the secret image into the blocks of the target audio. Stage 1.3 create a blank audio to create a mosaic audio. Stage 1.4 embedding the tile-image fitting information into the mosaic audio for later secret audio recovery. And the second phase includes two stages of operations: Stage 2.1 retrieving the previously-embedded tile audio fitting information from the mosaic audio. Stage 2.2 reconstructing the secret audio from the mosaic audio using the retrieved information. Fig 3 shows proposed a new type of art work can be used for secure keeping or covert communication of secret audio. This type of mosaic image is composed of small fragments of an input secret audio; and though all the fragments of the secret audio can be hear clearly, they are so tiny in size and so random in position that people cannot figure out what the source secret audio looks like. For creating the mosaic audio we need two audio named as source and target audio files. The first process is picking the target and the source audio for mosaic creation. Picking of target audio is similar to that of the Volume 4, Issue 2, March April 2015 Page 224

selected source audio but not the same. Then converting the lager source audio into several small audio for secret purpose. The splitting is used to placed the source audio into target audio, and doesn t play to the users. After that using the separate algorithm to find the most similar part of target image for placing the source tile audio. This would be done for all the split source audio. Then we are composing these audio in a mosaic form for hiding the secret information. The mosaic audio is same as that of the target audio but this contain the tile source audio not hear. The output audio will send to the destination, in destination re-mosaic process is held based on the some algorithm. The tile images are retrieving first for constructing the source image as send by the sender. After that the tile images are combined to create a original audio send by the sender. The other people can t hear the audio without having the mosaic reconstructing algorithm. The re-mosaic process is done by reverse process of the mosaic process. Algorithm Algorithm 1 Mosaic image creation Input: a secret image S, a target image T, and a secret key K. Output: a secret-fragment-visible mosaic image F. Steps: Stage 1. fitting the tile images into the target blocks. Step 1. If the size of the target image T is different from that of the secret image S, change the size of T to be identical to that of S; and divide the secret image S into n tile images {T1, T2,..., Tn} as well as the target image T into n target blocks {B1, B2,..., Bn} with each Ti or Bi being of size NT. Step 2. Compute the means and the standard deviations of each tile image Ti and each target block Bj for the three color channels according to (1) and (2); and compute accordingly the average standard deviations for Ti and Bj, respectively, for i = 1 through n and j = 1 through n. Step 3. Sort the tile images in the set Stile = {T1, T2,..., Tn} and the target blocks in the set Starget = {B1, B2,..., Bn} according to the computed average standard deviation values of the blocks; map in order the blocks in the sorted Stile to those in the sorted Starget in a 1-to-1 manner; and reorder the mappings according to the indices of the tile images, resulting in a mapping sequence L of the form: T1 Bj1, T2 Bj2,..., Tn Bjn. Step 4. Create a mosaic image F by fitting the tile images into the corresponding target blocks according to L. Stage 2. performing color conversions between the tile images and the target blocks. Step 5. Create a counting table TB with 256 entries, each with an index corresponding to a residual value, and assign an initial value of zero to each entry (note that each residual value will be in the range of 0 to 255). Step 6. For each mapping Ti Bji in sequence L, represent the means μc and μ _ c of Ti and Bji, respectively, by eight bits; and represent the standard deviation quotient qc appearing in (3) by seven bits, according to the scheme described in Section III(A) where c = r, g, or b. Step 7. For each pixel pi in each tile image Ti of mosaic image F with color value ci where c = r, g, or b, transform ci into a new value c i by (3); if c i is not smaller than 255 or if it is not larger than 0, then change c i to be 255 or 0, respectively; compute a residual value Ri for pixel pi by the way described in Section III(C); and increment by 1 the count in the entry in the counting table TB whose index is identical to Ri. Stage 3. rotating the tile images. Step 8. Compute the RMSE values of each color transformed tile image Ti in F with respect to its corresponding target block Bji after rotating Ti into each of the directions θ =0o, 90o, 180o and 270o; and rotate Ti into the optimal direction θo with the smallest RMSE value. Stage 4. embedding the secret image recovery information. Step 9. Construct a Huffman table HT using the content of the counting table TB to encode all the residual values computed previously. Step 10. For each tile image Ti in mosaic image F, construct a bit stream Mi for recovering Ti in the way as described in Section III(D), including the bit-segments which encode the data items of: 1) the index of the corresponding target block Bji; 2) the optimal rotation angle θ of Ti; 3) the means of Ti and Bji and the related standard deviation quotients of all three color channels; and 4) the bit sequence for overflows/underflows with residuals in Ti encoded by the Huffman table HT constructed in Step 9. Step 11. Concatenate the bit streams Mi of all Ti in F in a raster-scan order to form a total bit stream Mt ; use the secret key K to encrypt Mt into another bit stream M_t ; and embed M_ t into F by the reversible contrast mapping scheme proposed in [24]. Step 12. Construct a bit stream I including: 1) the number of conducted iterations Ni for embedding M_ t; 2) the number of pixel pairs Npair used in the last iteration; and 3) the Huffman table HT constructed for the residuals; and embed the bit stream I into mosaic image F by the same scheme used in Step 11. Algorithm 2 Secret image recovery Input: a mosaic image F with n tile images {T1, T2,...,Tn} and the secret key K. Output: the secret image S. Steps: Stage 1. extracting the secret image recovery information. Step 1. Extract from F the bit stream I by a reverse version of the scheme proposed in [24] and decode them to obtain the following data items: 1) the number of iterations Ni for embedding M_ t ; 2) the total number of used pixel pairs Npair in the last iteration; and 3) the Huffman table HT for encoding the values of the residuals of the overflows or underflows. Step 2. Extract the bit stream M_t using the values of Ni and Npair by the same scheme used in the last step. Step 3. Decrypt the bit stream M_t into Mt by K. Volume 4, Issue 2, March April 2015 Page 225

Step 4. Decompose Mt into n bit streams M1 through Mn for the n to-be-constructed tile images T1 through Tn in S, respectively. Step 5. Decode Mi for each tile image Ti to obtain the following data items: 1) the index ji of the block Bji in F corresponding to Ti; 2) the optimal rotation angle θ of Ti; 3) the means of Ti and Bji and the related standard deviation quotients of all color channels; and 4) the overflow/underflow residual values in Ti decoded by the Huffman table HT. Stage 2. recovering the secret image. Step 6. Recover one by one in a raster-scan order the tile images Ti, i = 1 through n, of the desired secret image S by the following steps: 1) rotate in the reverse direction the block indexed by ji, namely Bji, in F through the optimal angle θ and fit the resulting block content into Ti to form an initial tile image Ti; 2) use the extracted means and related standard deviation quotients to recover the original pixel values in Ti according to (4); 3) use the extracted means, standard deviation quotients, and (5) to compute the two parameters cs and cl; 4) scan Ti to find out pixels with values 255 or 0 which indicate that overflows or underflows, respectively, have occurred there; 5) add respectively the values cs or cl to the corresponding residual values of the found pixels; and 6) take the results as the final pixel values, resulting in a final tile image Ti. Step 7. Compose all the final tile images to form the desired secret image S as output. 4.Conclusion The proposed work which can be used for secure keeping or covert communication of secret audio. This type of mosaic audio is composed of small fragments of an input secret image and though all the fragments of the secret audio can be seen clearly, they are so tiny in size and so random in position that people cannot hear out what the source secret audio looks like. A novel algorithm has also been proposed for searching the tile audio in a secret audio for the most similar ones to fit the target blocks of a selected target audio more efficiently.. [4]. Wei-Jen Wang, Cheng-Ta Huang, and Shiuh-Jeng Wang, VQ Applications in Steganographic Data Hiding Upon Multimedia Images" IEEE Systems Journal, Vol. 5, No. 4, December 2011. [5]. Kotaro Yamamoto, Real-Time Audio Watermarking Based on Characteristics of PCM in Digital Instrument, in Journal of Information Hiding and Multimedia Signal Processing,2010 [6]. Hussein A. Aly, Data Hiding in Motion Vectors of Compressed Video Based on Their Associated Prediction Error, IEEE Trans On Information Forensics And Security, Vol. 6, No. 1, March 2011. [7]. Ersin Esen and A. Aydin Alatan,"Robust Video Data Hiding Using Forbidden Zone Data Hiding and Selective Embedding, in Trans On Circuits And Systems For Video Technology, Vol. 21, No. 8, August 2011. [8]. Zahra Toony and Mansour Jamzad,"A Novel Image Hiding Scheme Using Content Aware Seam Carving Method in International Conference on Availability, Reliability and Securityy, 2010. [9]. V.Rajkumar," Modifier Digital Image Steganography Using Discrete Wavelet Transform, in Trans On Circuits And Systems For Video Technology,Volume 1, Issue 1, March 2013 Nilanjan Dey, Anamitra Bardhan Roy and Sayantan Dey,"A Novel Approach of Color Image Hiding using RGB Color planes and DWT, in International Journal of Computer Applications (0975 8887), Volume 36 No.5, December 2011 REFERENCES [1]. Nutan Palshikar, Prof. Sanjay Jadhav, "Lossless Data Hiding using Histogram Modification and Hash Encryption Scheme ", in International Journal of Emerging Technology and Advanced Engineering,January 2014. [2]. Xinpeng Zhang, "Separable Reversible Data Hiding in Encrypted Image", in in Proc. IEEE Trans. on Information Forensics and security,vol. 7, No. 2, APRIL 2012. [3]. Ya-Lin Lee and Wen-Hsiang Tsai, A new secure image transmission technique via secret- fragmentvisible mosaic images by Nearly-reversible Color Tranformations, in IEEE Trans. 2013. Volume 4, Issue 2, March April 2015 Page 226