International Journal of Research and Innovation (IJRI)
International Journal of Research and Innovation in Electronics and Communication Engineering (IJRIECE) JOINT DATA HIDING AND COMPRESSION BASED ON SALIENCY AND SMVQ
S. Girish1, E. Balakrishna2, S. Rehana Banu3. 1 Research Scholar,Department of Electronics AndCommunication Engineering, Chiranjeevi Reddy Institute of Engineering and Technology, Anantapur, A. P, India. 2 Assistant Professor,Department of Electronics AndCommunication Engineering, Chiranjeevi Reddy Institute of Engineering and Technology, Anantapur, A. P, India. 3 Assistant Professor,Department of Electronics AndCommunication Engineering, Chiranjeevi Reddy Institute of Engineering and Technology, Anantapur, A. P, India.
Abstract Global interconnect planning becomes a challenge as semiconductor technology continuously scales. Because of the increasing wire resistance and higher capacitive coupling in smaller features, the delay of global interconnects becomes large compared with the delay of a logic gate, introducing a huge performance gap that needs to be resolved A novel equalized global link architecture and driver– receiver co design flow are proposed for high-speed and low-energy on-chip communication by utilizing a continuous-time linear equalizer (CTLE). The proposed global link is analyzed using a linear system method, and the formula of CTLE eye opening is derived to provide high-level design guidelines and insights. Compared with the separate driver–receiver design flow, over 50% energy reduction is observed. *Corresponding Author: S. Girish Research Scholar, Department of Electronics AndCommunication Engineering, Chiranjeevi Reddy Institute of Engineering and Technology,Anantapur, A. P, India. Email: girish.seela@gmali.com. Year of publication: 2016 Review Type: peer reviewed Volume: I, Issue : I Citation: S. Girish, Research Scholar"Joint Data Hiding And Compression Based On Saliency And Smvq" International Journal of Research and Innovation on Science, Engineering and Technology (IJRISET) (2016) 01-05 DATA HIDING FOR IMAGE AUTHENTICATION Introduction For years, audio, image, and video have played important role in journalism, archiving and litigation. King incident played an important role in prosecution, a secretly recorded conversation between Monica Lewinsky and Linda Tripp touched off the 1998 presidential impeachment, just to name a few. Keeping our focus on still pictures, we have no difficulty in realizing that the validity of the old saying “Picture never lies” has been challenged in the digital world of multimedia. Compared with the traditional analog media, making seamless alteration is much easier on digital media by software editing tools. With the popularity of consumer-level scanner, printer, digital camera, and digital camcorder, detecting tampering becomes an important concern. In this chapter, we discuss using digital watermarking techniques to partially solve this problem by embedding authentication data invisibly into digital images. In general, authenticity is a relative concept: whether an item is authentic or not is relative to a reference or certain type of representation that is regarded as authentic.
The following features are desirable to construct an effective authentication scheme 1. to be able to determine whether an image has been altered or not; 2. to be able to integrate authentication data with host image rather than as a separate data file; 3. the embedded authentication data be invisible under normal viewing conditions; 4. to be able to locate alteration made on the image; 5. to allow the watermarked image be stored in lossycompression format, or more generally, to distinguish moderate distortion that does not change the high-level content vs. content tampering. This chapter presents a general framework of watermarking for authentication and proposes a new authentication scheme by embedding a visually meaningful watermark and a set of features in the transform domain of an image via table look-up. The embedding is a Type-II embedding according to Chapter 3. Making use of the predistortion nature of Type-II embedding, our proposed approach can be applied to compressed image using JPEG or other compression techniques, and the watermarked image can be kept in the compressed format. The proposed approach therefore allows distinguishing moderate distortion that does not change the high-level content versus content tampering. The alteration made on the marked image can be also localized. These features make the proposed scheme suitable for building a “trustworthy” digital camera. We also demonstrate the use of shuffling (Chapter 4) in this specific problem to equalize uneven embedding capacity and to enhance both embedding rate and security. Previous Work The existing works on image authentication can be classified into several categories: digital signature based, pixeldomain embedding, and transform-domain embedding. The latter two categories are invisible fragile or semi-fragile watermarking. Digital signature schemes are built on
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International Journal of Research and Innovation (IJRI)
the ideas of hash (or called message digest) and public key encryption that were originally developed for verifying the authenticity of text data in network communication. Friedman extended it to digital image as follows. A signature computed from the image data is stored separately for future verification. This image signature can be regarded as a special encrypted checksum. It is unlikely that two different natural images have same signature, and even if a single bit of image data changes, the signature may be totally different. Furthermore, public-key encryption makes it very difficult to forge signature, ensuring ] a high security level. After his work, Schneider et al. [and Storck proposed content-based signature. They produce signatures from low-level content features, such as block mean intensity, to protect image content instead of the exact representation. Another content-signature approach by Lin et al. developed the signature based on a relation between coefficient pairs that is invariant before and after compression [41, 76]. Strictly speaking, these signature schemes do not belong to watermarking since the signature is stored separately instead of embedding into images. A STUDY OF VARIOUS IMAGE COMPRESSION TECHNIQUES IMAGE An image is essentially a 2-D signal processed by the human visual system. The signals representing images are usually in analog form. However, fo r processing, storage and transmission by computer applications, they are converted from analog to digital form. A digital image is basically a 2-Dimensional array of pixels. Images form the significant part of data, particularly in remote sensing, biomedical and video conferencing applications. The use of and dependence on information and computers continue to grow, so too does our need for efficient ways of storing and transmitting large amounts of data. IMAGE COMPRESSION Image compression addresses the problem of reducing the amount of data required to represent a digital image. It is a process intended to yield a compact representation of an image, thereby reducing the image storage/transmission requirements. Compression is achieved by the removal of one or more of the three basic data redundancies:
SALIENCY MODEL Automatic scene analysis In the last decade, security cameras have become a common sight in the urban landscape. The ability to monitor a number of different places from one location has aided crime prevention. A vast number of security cameras are being installed in everything from public streets to train carriages, which has spawned a fundamental problem. On the London Underground alone, there are at least 9000 security cameras. Security staff can have as many as 60 cameras to watch at any one time so monitoring is an extremely difficult task, requiring considerable concentration for long periods of time (Donald, 1999). It is easy to imagine that manpower on its own is not enough to deal with the vast quantities of data that are being recorded. Automation is clearly the next step. INFORMATION HIDING USING VECTOR QUANTIZATION In the earlier part of the thesis different methods in the spatial domain and transform domain are studied This chapter deals with the techniques for hiding Information in the compressed domain. One of the most commonly studied image compression technique is Vector Quantization (VQ)[60], which is a lossy image compression technique based on the principle of block coding. VQ is a clustering technique & every cluster is represented by a codevector. It is widely used to compress grey-level images because of its low bit rate. The main concept of VQ is to utilize templates instead of blocks to do the image compression. These templates, also referred to as codewords or codevectors, are stored in a codebook, and the codebook is shared only between the sender and the receiver. Hence, the index value of the template is used to represent all the pixel values of the block so that data compression can be achieved. Such a mechanism is extremely easy to implement although the organization of the templates affects the quality of the compressed image. Experimental Results: Table Average values of PSNR, MSE and AFCPV using 1 bit , 2, 3, 4, and variable bits for Information hiding in Vector Quantized codebook method on LBG, KPE, KMCG and KFCG codebook is of size 2048
1.Coding Redundancy 2.Interpixel Redundancy 3.Psychovisual Redundancy Coding redundancy is present when less than optimal code words are used. Interpixel redundancy results from correlations between the pixels of an image. Psychovisual redundancy is due to data that is ignored by the human visual system (i.e. visually non essential information). BENEFITS OF COMPRESSION • It provides a potential cost savings associated with sending less data over switched telephone • It not only reduces storage requirements but also overall execution time. • It also reduces the probability of transmission errorssince fewer bits are transferred. • It also provides a level of security against illicit monitoring.
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International Journal of Research and Innovation (IJRI)
Remark: It is observed that KFCG performs better than LBG, KPE and KMCG considering MSE, PSNR and AFCPV. Figure shows the results for cover image Lioness and secret image work logo
with the restoration of films, the second one is related to texture synthesis, and the third one, a significantly less studied class though very influential to the work here presented, is related to disocclusion. Joyeux et al. [4] devised a 2-steps frequency based reconstruction system for detecting and removing line scratches in films. They propose to first recover low and then high frequencies. Although good results are obtained for their specific application, the algorithm can not handle large loss areas. Frequency domain algorithms trade a good recovery of the overall structure of the regions for poor spatial results regarding, for instance, the continuity of lines. Kokaram et al. [6] use motion estimation and autoregressive models to interpolate losses in films from adjacent frames. The basic idea is to copy into the gap the right pixels from neighboring frames. The technique can not be applied to still images or to films where the regions to be inpainted span many frames. Our contribution
Remark: It is observed that Stego is similar to the original image using any of the four codebook generation algorithms. Table shows the hiding capacity for all covers and messages for 1,2,3,4 and variable bits for all 4 Codebook generation techniques Figure 6.4, 6.5, 6.6 and 6.7 show the hiding capacity, PSNR, MSE and AFCPV for all 4 algorithms and 1,2,3,4 and variable bit hiding method. Table Hiding Capacity in bits using 1 bit, 2 bits, 3 bits, 4 bits, and variable bits method on LBG, KPE, KMCG and KFCG codebook of size 2048.
Algorithms devised for film restoration are not appropriate for our application since they normally work on relatively small regions and rely on the existence of information from several frames. On the other hand, algorithms based on texture synthesis can fill large regions, but require the user to specify what texture to put where. This is a significant limitation of these approaches, as may be seen in examples presented later in this paper, where the region to be inpainted is surrounded by hundreds of different backgrounds, some of them being structure and not texture. The technique we propose does not require any user intervention, once the region to be inpainted has been selected. The algorithm is able to simultaneously fill regions surrounded by different backgrounds, without the user specifying “what to put where.� No assumptions on the topology of the region to be inpainted, or on the simplicity of the image, are made. The algorithm is devised for inpainting in structured regions (e.g., regions crossing throughboundaries), though it is not devised to reproduce large textured areas. As we will discuss later, the combination of our proposed approach with texture synthesis techniques is the subject of current research. RESULTS
Related work and our contribution We should first note that classical image denoising algorithms do not apply to image inpainting, since the regions to be inpainted are usually large. That is, regions occupied by top to bottom scratches along several film frames, long cracks in photographs, superimposed large fonts, and so on, are of significant larger size than the type of noise assumed in common image enhancement algorithms. In addition, in common image enhancement applications, the pixels contain both information about the real data and the noise, while in image in painting, there is no significant information in the region to be inpainted. The information is mainly in the regions surrounding the areas to be inpainted. There is then a need to develop specific techniques to address these problems. Mainly three groups of works can be found in the literature related to digital in painting. The first one deals
Input Image
The image is given to the saliency extraction block. The following is the output of Saliency.
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International Journal of Research and Innovation (IJRI)
CONCLUSION AND FUTURE SCOPE
Saliency output of the input image
In this project new methods of Information hiding in compressed domain using Vector Quantization and SMVQ are proposed. They are Information Hiding in salient images using Vector Quantized Codebook and SMVQ. In this paper, we proposed a joint data-hiding and compression scheme by using SMVQ and PDE-based image inpainting and saliency detection. The blocks, except for those in the leftmost and topmost of the image, can be embedded with secret data and compressed simultaneously, and the adopted compression method switches between SMVQ and image inpainting adaptively according to the embedding bits. VQ is also utilized for some complex blocks to control the visual distortion and error diffusion. On the receiver side, after segmenting the compressed codes into a series of sections by the indicator bits, the embedded secret bits can be easily extracted according to the index values in the segmented sections, and the decompression for all blocks can also be achieved successfully by VQ, SMVQ, and image inpainting. The existing code book generation can be improved and produce better results. Some other techniques include. The data size should be increased than the present. REFERENCES [1] W. B. Pennebaker and J. L. Mitchell, The JPEG Still Image Data Compression Standard. New York, NY, USA: Reinhold, 1993.
Saliency MAP. Image divided into salient part and non salient part.
Then the original image is Compressed image using data SMVQ and VQ along with data hiding. The following the output.
[2] D. S. Taubman and M. W. Marcellin, JPEG2000: Image Compression Fundamentals Standards and Practice. Norwell, MA, USA: Kluwer, 2002. [3] A. Gersho and R. M. Gray, Vector Quantization and Signal Compression. Norwell, MA, USA: Kluwer, 1992. [4] N. M. Nasrabadi and R. King, “Image coding using vector quantization: A review,” IEEE Trans. Commun., vol. 36, no. 8, pp. 957–971, Aug. 1988. [5] Announcing the Advanced Encryption Standard (AES), National Institute of Standards & Technology, Gaithersburg, MD, USA, Nov. 2001. [6] R. L. Rivest, A. Shamir, and L. Adleman, “A method for obtaining digital signatures and public-key cryptosystems,” Commun. ACM, vol. 21, no. 2, pp. 120–126, 1978. [7] F. A. P. Petitcolas, R. J. Anderson, and M. G. Kuhn, “Information hiding survey,” Proc. IEEE, vol. 87, no. 7, pp. 1062–1078, Jul. 1999.
Compressed Image
[8] C. D. Vleeschouwer, J. F. Delaigle, and B Macq, “Invisibility and application functionalities in perceptual watermarking: An overview,” Proc. IEEE, vol. 90, no. 1, pp. 64–77, Jan. 2002. [9] C. C. Chang, T. S. Chen, and L. Z. Chung, “A steganographic method based upon JPEG and quantization table modification,” Inf. Sci., vol. 141, no. 1, pp. 123–138, 2002. [10] H. W. Tseng and C. C. Chang, “High capacity data hiding in JPEGcompressed images,” Informatica, vol. 15, no. 1, pp. 127–142, 2004.
The final Reconstructed image at the output.
[11] P. C. Su and C. C. Kuo, “Steganography in JPEG2000 compressed images,” IEEE Trans. Consum. Electron., vol. 49, no. 4, pp. 824–832, Nov. 2003.
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[12] W. J. Wang, C. T. Huang, and S. J. Wang, “VQ applications in steganographic data hiding upon multimedia images,” IEEE Syst. J., vol. 5, no. 4, pp. 528–537, Dec. 2011.
[22] C. C. Lee, W. H. Ku, and S. Y. Huang, “A new steganographic scheme based on vector quantisation and search-order coding,” IET Image Process., vol. 3, no. 4, pp. 243–248, 2009.
[13] Y. C. Hu, “High-capacity image hiding scheme based on vector quantization,” Pattern Recognit., vol. 39, no. 9, pp. 1715–1724, 2006.
[23] S. C. Shie and S. D. Lin, “Data hiding based on compressed VQ indices of images,” Comput. Standards Inter., vol. 31, no. 6, pp. 1143–1149, 2009.
[14] Y. P. Hsieh, C. C. Chang, and L. J. Liu, “A two-codebook combination and three-phase block matching based image-hiding scheme with high embedding capacity,” Pattern Recognit., vol. 41, no. 10, pp. 3104–3113, 2008.
[24] C. C. Chang, G. M. Chen, and M. H. Lin, “Information hiding based on search-order coding for VQ indices,” Pattern Recognit. Lett., vol. 25, no. 11, pp. 1253–1261, 2004.
[15] C. H. Yang and Y. C. Lin, “Fractal curves to improve the reversible data embedding for VQ-indexes based on locally adaptive coding,” J. Vis. Commun. Image Represent., vol. 21, no. 4, pp. 334–342, 2010.
[25] T. Kim, “Side match and overlap match vector quantizers for images,” IEEE Trans. Image Process., vol. 1, no. 2, pp. 170–185, Apr. 1992. AUTHOR
[16] Y. Linde, A. Buzo, and R. M. Gray, “An algorithm for vector quantization design,” IEEE Trans. Commun., vol. 28, no. 1, pp. 84–95, Jan. 1980. [17] C. C. Chang and W. C. Wu, “Fast planar-oriented ripple search algorithm for hyperspace VQ codebook,” IEEE Trans. Image Process., vol. 16, no. 6, pp. 1538–1547, Jun. 2007. [18] W. C. Du and W. J. Hsu, “Adaptive data hiding based on VQ compressed images,” IEE Proc. Vis., Image Signal Process., vol. 150, no. 4, pp. 233–238, Aug. 2003. [19] C. C. Chang and W. C. Wu, “Hiding secret data adaptively in vector quantisation index tables,” IEE Proc. Vis., Image Signal Process., vol. 153, no. 5, pp. 589–597, Oct. 2006. [20] C. C. Lin, S. C. Chen, and N. L. Hsueh, “Adaptive embedding techniques for VQ-compressed images,” Inf. Sci., vol. 179, no. 3, pp. 140–149, 2009. [21] C. H. Hsieh and J. C. Tsai, “Lossless compression of VQ index with search-order coding,” IEEE Trans. Image Process., vol. 5, no. 11, pp. 1579–1582, Nov. 1996.
S. Girish,
Research Scholar, Department of Electronics AndCommunication Engineering, Chiranjeevi Reddy Institute of Engineering and Technology, Anantapur, A. P, India.
E. Balakrishna,
Assistant Professor, Department of Electronics AndCommunication Engineering, Chiranjeevi Reddy Institute of Engineering and Technology, Anantapur, A. P, India.
S. Rehana Banu ,
Assistant Professor, Department of Electronics AndCommunication Engineering, Chiranjeevi Reddy Institute of Engineering and Technology, Anantapur, A. P, India.
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