![2
International Journal of Research and Innovation (IJRI)
the ideas of hash (or called message digest) and public
key encryption that were originally developed for verify-
ing the authenticity of text data in network communica-
tion. Friedman extended it to digital image as follows. A
signature computed from the image data is stored sepa-
rately 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 sig-
nature may be totally different. Furthermore, public-key
encryption makes it very difficult to forge signature, en-
suring ] 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 hu-
man visual system. The signals representing images are
usually in analog form. However, fo r processing, stor-
age 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, bi-
omedical 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/trans-
mission requirements. Compression is achieved by the
removal of one or more of the three basic data redundan-
cies:
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 send-
ing less data over switched telephone
• It not only reduces storage requirements but also overall
execution time.
• It also reduces the probability of transmission errorss-
ince fewer bits are transferred.
• It also provides a level of security against illicit monitor-
ing.
SALIENCY MODEL
Automatic scene analysis
In the last decade, security cameras have become a com-
mon 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 con-
centration 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 re-
corded. Automation is clearly the next step.
INFORMATION HIDING USING VECTOR QUANTIZA-
TION
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 Quan-
tization (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 im-
ages 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 code-
words or codevectors, are stored in a codebook, and the
codebook is shared only between the sender and the re-
ceiver. 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 ex-
tremely 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](https://image.slidesharecdn.com/ijri-ece-01-01jointdatahidingandcompressionbasedonsaliencyandsmvq-161122075029/85/Ijri-ece-01-01-joint-data-hiding-and-compression-based-on-saliency-and-smvq-2-320.jpg)
![3
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 se-
cret image work logo
Remark: It is observed that Stego is similar to the
original image using any of the four codebook genera-
tion algorithms.
Table shows the hiding capacity for all covers and mes-
sages for 1,2,3,4 and variable bits for all 4 Codebook gen-
eration techniques Figure 6.4, 6.5, 6.6 and 6.7 show the
hiding capacity, PSNR, MSE and AFCPV for all 4 algo-
rithms 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.
Related work and our contribution
We should first note that classical image denoising al-
gorithms do not apply to image inpainting, since the re-
gions 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 sur-
rounding 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 lit-
erature related to digital in painting. The first one deals
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 pre-
sented, is related to disocclusion. Joyeux et al. [4] devised
a 2-steps frequency based reconstruction system for de-
tecting 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 interpo-
late 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
Algorithms devised for film restoration are not appropri-
ate for our application since they normally work on rela-
tively small regions and rely on the existence of informa-
tion from several frames. On the other hand, algorithms
based on texture synthesis can fill large regions, but re-
quire 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 dif-
ferent 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, with-
out the user specifying “what to put where.” No assump-
tions 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
Input Image
The image is given to the saliency extraction block. The following is the
output of Saliency.](https://image.slidesharecdn.com/ijri-ece-01-01jointdatahidingandcompressionbasedonsaliencyandsmvq-161122075029/85/Ijri-ece-01-01-joint-data-hiding-and-compression-based-on-saliency-and-smvq-3-320.jpg)
![4
International Journal of Research and Innovation (IJRI)
Saliency output of the input image
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.
Compressed Image
The final Reconstructed image at the output.
CONCLUSION AND FUTURE SCOPE
In this project new methods of Information hiding in com-
pressed domain using Vector Quantization and SMVQ
are proposed. They are Information Hiding in salient im-
ages 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 embed-
ding 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.
[2] D. S. Taubman and M. W. Marcellin, JPEG2000: Im-
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[3] A. Gersho and R. M. Gray, Vector Quantization and
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[5] Announcing the Advanced Encryption Standard (AES),
National Institute of Standards & Technology, Gaithers-
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[6] R. L. Rivest, A. Shamir, and L. Adleman, “A method
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![5
International Journal of Research and Innovation (IJRI)
[12] W. J. Wang, C. T. Huang, and S. J. Wang, “VQ appli-
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AUTHOR
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.](https://image.slidesharecdn.com/ijri-ece-01-01jointdatahidingandcompressionbasedonsaliencyandsmvq-161122075029/85/Ijri-ece-01-01-joint-data-hiding-and-compression-based-on-saliency-and-smvq-5-320.jpg)
Ijri ece-01-01 joint data hiding and compression based on saliency and smvq
- 1. 1 International Journal of Research and Innovation (IJRI) 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. *Corresponding Author: S. Girish Research Scholar, Department of Electronics AndCommunication Engi- neering, 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" Inter- national 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 impor- tant role in journalism, archiving and litigation. King in- cident played an important role in prosecution, a secretly recorded conversation between Monica Lewinsky and Linda Tripp touched off the 1998 presidential impeach- ment, just to name a few. Keeping our focus on still pic- tures, 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 tradi- tional 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 be- comes 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 re- garded as authentic. The following features are desirable to construct an effec- tive 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 lossy- compression 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 watermark- ing 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 com- pression 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 dem- onstrate 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 classi- fied into several categories: digital signature based, pixel- domain embedding, and transform-domain embedding. The latter two categories are invisible fragile or semi-frag- ile watermarking. Digital signature schemes are built on 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 lin- ear 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. International Journal of Research and Innovation in Electronics and Communication Engineering (IJRIECE)
- 2. 2 International Journal of Research and Innovation (IJRI) the ideas of hash (or called message digest) and public key encryption that were originally developed for verify- ing the authenticity of text data in network communica- tion. Friedman extended it to digital image as follows. A signature computed from the image data is stored sepa- rately 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 sig- nature may be totally different. Furthermore, public-key encryption makes it very difficult to forge signature, en- suring ] 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 hu- man visual system. The signals representing images are usually in analog form. However, fo r processing, stor- age 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, bi- omedical 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/trans- mission requirements. Compression is achieved by the removal of one or more of the three basic data redundan- cies: 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 send- ing less data over switched telephone • It not only reduces storage requirements but also overall execution time. • It also reduces the probability of transmission errorss- ince fewer bits are transferred. • It also provides a level of security against illicit monitor- ing. SALIENCY MODEL Automatic scene analysis In the last decade, security cameras have become a com- mon 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 con- centration 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 re- corded. Automation is clearly the next step. INFORMATION HIDING USING VECTOR QUANTIZA- TION 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 Quan- tization (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 im- ages 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 code- words or codevectors, are stored in a codebook, and the codebook is shared only between the sender and the re- ceiver. 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 ex- tremely 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
- 3. 3 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 se- cret image work logo Remark: It is observed that Stego is similar to the original image using any of the four codebook genera- tion algorithms. Table shows the hiding capacity for all covers and mes- sages for 1,2,3,4 and variable bits for all 4 Codebook gen- eration techniques Figure 6.4, 6.5, 6.6 and 6.7 show the hiding capacity, PSNR, MSE and AFCPV for all 4 algo- rithms 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. Related work and our contribution We should first note that classical image denoising al- gorithms do not apply to image inpainting, since the re- gions 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 sur- rounding 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 lit- erature related to digital in painting. The first one deals 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 pre- sented, is related to disocclusion. Joyeux et al. [4] devised a 2-steps frequency based reconstruction system for de- tecting 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 interpo- late 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 Algorithms devised for film restoration are not appropri- ate for our application since they normally work on rela- tively small regions and rely on the existence of informa- tion from several frames. On the other hand, algorithms based on texture synthesis can fill large regions, but re- quire 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 dif- ferent 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, with- out the user specifying “what to put where.” No assump- tions 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 Input Image The image is given to the saliency extraction block. The following is the output of Saliency.
- 4. 4 International Journal of Research and Innovation (IJRI) Saliency output of the input image 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. Compressed Image The final Reconstructed image at the output. CONCLUSION AND FUTURE SCOPE In this project new methods of Information hiding in com- pressed domain using Vector Quantization and SMVQ are proposed. They are Information Hiding in salient im- ages 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 embed- ding 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. [2] D. S. Taubman and M. W. Marcellin, JPEG2000: Im- age 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 vec- tor 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, Gaithers- burg, MD, USA, Nov. 2001. [6] R. L. Rivest, A. Shamir, and L. Adleman, “A method for obtaining digital signatures and public-key cryptosys- tems,” 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. [8] C. D. Vleeschouwer, J. F. Delaigle, and B Macq, “Invis- ibility and application functionalities in perceptual wa- termarking: 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 steg- anographic 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. [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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