155
CHAPTER 8
Conclusions and Future Work
The growth of modern communication needs a special means of security especially on
computer network. As there appears a risk that the sensitive information transmitted
might be intercepted or distorted by unintended observers for the openness of the
internet. So it has resulted in an explosive growth in secure communication and
information hiding. Moreover, the information hiding technique can be used
extensively in applications like business, military, commercials, anti-criminal, digital
forensic and so on. Steganography is the technique of secret communication which
has received much attention. In this thesis image based steganography methods have
been proposed to increase the performance of the data hiding techniques. This thesis
focuses on the analysis and development of image steganography techniques that can
hide data with a low detection rate and high payload.
8.1 Summary of contributions
The main contribution of this thesis is providing an enhanced image based
steganographic technique for achieving the goal of data hiding using steganography.
To achieve this goal the various existing image based steganographic techniques i.e.
spatial domain based and frequency domain based with an application to data hiding
have been investigated. Some of the methods related to such domain available in
literature are discussed in chapter 2. All digital file formats can be used for
steganography, but the formats those are with a high degree of redundancy are more
suitable. The redundant bits of an object are those bits that can be altered without the
alteration being detected easily. Chapter 3 presents a study of the different file
formats that can be used in steganography. The most popular cover objects used for
steganography are digital images. Digital images often have a large amount of
redundant data, and this is what steganography uses to hide the message. In Chapter 4,
the techniques related to image based steganography on both spatial domain and
Conclusions and Future Work
156
frequency domain are investigated to understand the details of the basic working
process. Along with it, the attacks and techniques related to image steganalysis are
discussed. The basic evaluation measures of the image based steganography are also
discussed that are used to examine the performance of a steganographic technique.
Related to all of the above facts, the existing steganographic algorithms are expanded
by combining them with the cryptographic process. In the first case the cryptography
and DCT based steganography is combined to form a process that holds the features
of steganographic and cryptography technique to increase the security of the secret
data as stated in Chapter 5. The secret message is first encrypted using substitution
cipher method. Then the cover-image which is to hold the secret data is preprocessed
to reduce noise present in the cover-image and increase the dependence between
neighboring pixels, so that the embedding process may better utilize the bits. The
encrypted message is then embedded in the DCT coefficients of high frequencies of
the cover-image. In the process of embedding a modified standard quantization table
is used by putting ones (1s) in the coefficients located in the high frequency part. The
process of extracting the secret message from the resulting stego-image is also stated.
In the experimental analysis, a comparative study of the proposed method with Jpeg–
Jsteg (Hsu and Wu, 1999) and Chang et al., 2002 based steganography is also
conducted. The hiding capacity of the proposed method is more than Jpeg–Jsteg and
Chang et al., 2002 and also shows better PSNR values than both the methods. Along
with this, results of the various other evaluation parameters are also discussed.
In Chapter 6, the existing LSB based steganography is also combined with encryption
technique to enhance the embedding capacity of image steganography. The secret
message undergoes double encryption firstly using transposition cipher method and
then with substitution method before embedding into the cover-image file. Then
encrypted message is embedded into an image by using least-significant-bit (LSB)
technique that enables high capacity of data embedding. Once all the message
characters are embedded into the cover-image, the target character is inserted in the
pixel of the cover-image immediately next to the one containing the last input
character of the message. The process of extraction of secret message is also stated.
The main security lies in the encryption method where the secret message that is to be
embedded goes though double encryption process and the encryption process is
Conclusions and Future Work
157
controlled by two different keys. In examining the performance of the proposed
steganographic technique, an evaluation scheme for steganographic system is
conducted using various performance parameters on various images. A comparative
study is also conducted with OPAP (Chan and Cheng, 2004) based on embedding bits
of secret message in the cover images. From both the experiments it is found that the
proposed method shows better PSNR than OPAP. From other experiments it is found
that the proposed method shows better PSNR. Various evaluation measures are also
performed to test the proposed method.
Chapter 7 introduces an approach of least significant bit (LSB) based steganography
in digital images that can override some statistical and structural measures of
detection by spreading message bits randomly in which the secret messages are
embedded only in the red plane of the cover-image’s pixel determined by a
pseudorandom number generator (PRNG) initiated by a stego-key. In the process of
LSB embedding process, the random number generator selects the hiding points in the
pixel’s red plane of the cover image by using a random interval method. The random
interval produces a random sequence of locations of the secret data. In this method the
red plane is selected for data embedding while the blue and green planes are left
unmodified. So after performing the embedding operation the unmodified green and
blue planes are added to the modified red plane to form the final stego-image. In
extraction of the secret message, only the pixel’s red plane is selected using the same
stego-key that produces the same sequence of random locations of the secret bits. In
the experimental analysis, secret message of fixed and variable sizes using different
stego-keys are used to embed into the cover-images for examining the effect of
increasing key values and message size. A comparison of the proposed method is also
done with Amirtharajan et al. 2013 scheme using PSNR and MSE as performance
measure. It is seen that the PSNR value dynamically changes with the change in key
value in Amirtharajan et al. scheme whereas in the proposed method the PSNR value
are mostly static as the PSNR value are all same for different stego-keys. The method
is also evaluated using histogram analysis, visualizing LSB bit-plane and various
other performance measures to examine its ability to withstand from attacks.
In this thesis, the focus is not only on the embedding strategy, but also on the pre-
processing stages, such as secret message encryption and embedding area selection to
Conclusions and Future Work
158
improve the security. A comparative study of the three proposed method is presented
in table 8.1 based on certain criteria which are used in the process of their
development.
Table 8.1(a): Comparative study of the proposed image steganography methods
Criteria DCT with
encryption
LSB with
encryption
Random LSB
Basic operation Encrypts the secret
message and pre-
process the cover-
image before
embedding data in
the high frequency
coefficients of DCT
Encrypts the secret
message using two
encryption technique
and then embeds
data in the LSB of
the cover-image
Embedding operates
only in the red plane
of the cover-image’s
pixel by modifying its
LSB determined by a
pseudorandom
number generator
Operates in the
Image domain
Frequency domain Spatial domain Spatial domain
Image compression
used
Lossy Lossless Lossless
Pre-processing of
cover-image
Yes No No
Data embedding
process
Sequential Sequential Scattering(random)
Key used Key is used only in
the encryption
process
Key is used only in
the encryption
process
Stego-key is used for
random pixel
selection in the cover-
image
Embeds secret data
in
High frequency
coefficients of the
cover-image
Red, green and blue
plane of the cover-
image
Only in red plane of
the pre-selected pixel
of the cover-image
Encryption of secret
data
Yes Yes No
Conclusions and Future Work
159
Table 8.1(b): Comparative study of the proposed image steganography methods
Criteria DCT with
encryption
LSB with
encryption
Random LSB
Robustness against
attacks
Good Good Excellent
Invisibility of Secret
data
Highly invisible Highly invisible Highly invisible
Capacity Average in
comparison to other
two methods
High High
Stego-image size Lower than cover-
image
Same as cover-image Same as cover-image
Number of secret key
used
One key in
encryption
Two keys, one for
transposition and
substitution
encryption
One stego-key is
used for random
selection of pixels
Extraction process
depends on
Embedding and
encryption process
Embedding and
encryption process
Embedding process
and the stego-key
PSNR value Average High High
Overall Security Secure Secure Highly secure
Distribution of secret
data on the cover-
image
It embeds secret
data in order.
It embeds secret data
in order
Spreads throughout
the cover-image
Average PSNR value
for capacity of 28 K.B
42 dB 57 dB 62 dB
Time Computation
(For embedding and
extraction)
More Less Less
Key used in extraction Key is used in
decryption process
Two Keys are used in
decryption process
Key is used in
extracting the pixels
consisting of secret
data
Conclusions and Future Work
160
8.2 Scope for future Work
The world of digital media is in a continuous state of evolution. Steganography is
regarded as technology that has major competitive applications. While a significant
progress in the image steganography techniques has been achieved, still there is scope
for the improvement as there is yet to be evolved a standard method and the proposed
algorithms can be further enhanced. In this thesis, the study and analysis related to the
image based steganography relating to LSB and DCT has been done. The
enhancements can be done by using soft computing techniques such as Neural based
steganography, Fuzzy and Genetic algorithms based approaches. The future work can
also take into considerations of the Quantum computation approaches which can
extend the classical steganography for performance enhancement of the existing
techniques.
The existing transform and spatial domain based approaches can be enhanced with
certain variations. The DCT and DWT techniques can also be enhanced by using
randomization approach where the secret bits can be embedded randomly selected
blocks. Additionally, improving the embedding capacity of these methods that can
withstand severe compression can be considered. In the spatial domain enhancement
can also be done. The LSB based random embedding where the secret data is
embedded only in red plane can be enhanced using two planes for embedding (Red,
Green or blue plane) that will increase the embedding capacity and will also preserve
the security. The embedding capacity can also be enhanced by using more LSBs and
maintaining the statistical properties of the images.
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183
Appendix A
Definition of Terms
Artifact: Artifacts are the irregularities that may be present in an image after
processing. They are not related to the details of the image and sometimes
accompany transmitted signals.
Bit-plane: The bit-plane of an image is a set of bits corresponding to a given bit
position in each of the binary numbers representing the image.
Carrier: Carriers are the digital media that are used as a medium to carry the secret
data. It is the medium into which the secret data is hidden.
Ciphertext: It is the translated text which is formed by encrypting the secret data. It
is the unordered or substituted text created by changing its readability and
meaning.
Coefficient: Coefficients are formed when a signal is transformed a from an image
representation into a frequency representation by using discrete transformation.
Cover-image: The image which is taken as a medium of covering the secret data. It is
the original image into which the secret data is inserted into its redundant bits.
Cryptanalysis: It is the study of attacks on the cryptography to find weaknesses in
them, without necessarily knowing the key or the algorithm.
Decibel: The decibel (dB) is a logarithmic unit used to express the ratio between two
values of a physical quantity. It is used to measure signal level after processing
and is widely used in electronics, signals and communication.
Decryption: Decryption is the process of taking encoded or encrypted text and
converting it back into original plaintext which are understandable.
Eavesdropper: Eavesdroppers are the attackers (unauthorized person) who try to
break a signal through communication channel to check if the signal contains any
secret data.
Appendix A
184
Encryption: The process of transformation from plaintext (secret message) to
ciphertext (unreadable format) to create a data that is not understandable.
Entropy coding: Entropy coding encodes an image by rounding the coefficient
values to integer to reduce the size. Entropy coding is lossless compression.
Fidelity: Fidelity is the perceptual similarity between images before and after
processing.
Keystream generator: It generates random sequence of numbers by partitioning the
stego-key into random sequences. These sequences allocate positions of the
secret bits.
MSE: Mean squared error (MSE) shows variation between the cover- image and
resultant (stego) image. A high quality image should have less MSE value.
Payload: In steganography, payload relates to the amount of secret data that can be
hidden into digital media.
Pixel indices: Pixel indices are the random locations of the pixels which are formed
using random number generator.
Plaintext: It is the original secret message that is transformed into unordered state
using encryption process.
PRNG: The pseudorandom number generator (PRNG) generates sequence of random
numbers using a stego-key which is used to select random pixels of the cover-
image for hiding the secret data. The key is used as seed for the random number
generation.
PSNR: The Peak-Signal-to-Noise Ratio (PSNR) is the performance measurement
criteria that show the relationship between the bit- or detection-error of two
similar signals. A high quality image should have higher PSNR value.
Randomization: It is the process of scattering the secret bits in different pixel
position of the cover-image that makes the secret data to be in random order.
Seed: The seed defines the starting point of a random number generator. It initiates
the process of random number generation.
Appendix A
185
Steganalysis: Steganalysis is the technique to detect whether a given digital media
contains hidden data. The steganalysis plays a role in the selection of features or
properties of the digital media to test for suspicious data.
Steganalyst: Steganalyst is the individual (attacker) who performs the steganalysis
with the purpose of detecting suspicious or secret data into a medium when
transmitted.
Stego-image: The resultant image formed as a result of the steganography algorithm
which contains secret data embedded into it.
Stego-key: The secret key used in the steganographic method to choose the random
pixel position in the image. The security of a stego-key
Target character: It is the last character that is embedded into the cover-image. The
target character terminates the embedding and extraction process.
Zigzag: Zigzag order is performed to group similar frequencies together by sorting
the coefficients in zigzag ordering.
186
Appendix B
List of Publications
Journals
1. Laskar, S.A. and Hemachandran, K. (2012). An Analysis of Steganography
and Steganalysis Techniques, Assam University Journal of Science and
Technology, ISSN: 0975-2773, Vol. 9, No. 2, pp. 88-103.
2. Laskar, S.A. and Hemachandran, K. (2012). High Capacity data hiding using
LSB Steganography and Encryption. International Journal of Database
Management Systems (IJDMS), ISSN: 0975-5705, Vol. 4, No. 6, pp. 57-68.
3. Laskar, S.A. and Hemachandran, K. (2013). Steganography Based on Random
Pixel Selection for Efficient Data Hiding. International journal of Computer
Engineering and Technology (IJCET), ISSN: 0976-6367, Vol. 4, Issue 2, pp.
31-44.
4. Laskar, S.A. and Hemachandran, K. (2014). A Review on Image Steganalysis
techniques for attacking Steganography”, International Journal of
Engineering Research & Technology (IJERT), ISSN: 2278-0181, Vol. 3, Issue
1, pp. 3400-3410.
Book Chapters
1. Laskar, S.A. and Hemachandran, K. Combining JPEG Steganography and
Substitution Encryption for Secure Data Communication. In David C. Wyld,
et al. (Eds). Computer Science & Information Technology (CS & IT): ISSN:
2231-5403, CCSEA, SEA, CLOUD, DKMP, CS & IT 05, pp. 149–160,
(2012).
Appendix B
187
Conference and Symposium
1. Laskar, S.A. and Hemachandran, K. “Combining JPEG Steganography and
Substitution Encryption for Secure Data Communication” has been presented
in Second International Conference on Computer Science, Engineering and
Applications (CCSEA)-2012, CCSEA, SEA, CLOUD, DKMP, CS & IT 5,
Organized by AIRCC, May, 26-27, 2012, Delhi, India.
Citation Count -
(a) Laskar, S.A. and Hemachandran, K. (2012). An Analysis of Steganography
and Steganalysis Techniques, Assam University Journal of Science and
Technology, ISSN: 0975-2773, Vol.9, No-2, pp. 88-103. Cited by 1
Cited by
1. Maan, V. K. and Dhaliwal, H. S. (2013). Vector Quantization in Image
Steganography. International Journal of Engineering Research & Technology
(IJERT) Vol. 2, No. 4 (2013), pp. 421-424.
(b) Laskar, S.A. and Hemachandran, K. (2012). High Capacity data hiding using
LSB Steganography and Encryption." International Journal of Database
Management Systems (IJDMS), ISSN: 0975-5705, Vol.4, No. 6, pp. 57-68.,
DOI: 10.5121/ijdms.2012.4605. Cited by 5
Cited by
1. Bansal, T. and Lamba, R. (2013). Steganography on Colour Images using
32x32 Quantization Table. International Journal for Advance Research in
Engineering and Technology (IJARET), Volume 1, Issue V, 2013, pp. 68-72,
ISSN: 2320 6802.
2. Vasudev, P. and Saurabh, K. (2013). Video Steganography Based on Improved
DCT 32*32 Vector Quantization Method. International Journal of Software
& Hardware Research in Engineering (IJSHRE), ISSN: 2347-4890, Vol. 1, No.
4, 2013, pp. 46-51.
Appendix B
188
3. Garg, T. and Vatta, S. (2014). A Review on Data Compression using
Steganography. International Journal of Computer Science and Mobile
Computing (IJCSMC), Vol.3 Issue.6, June- 2014, pg. 275-278, ISSN 2320–
088X.
(c) Laskar, S.A. and Hemachandran, K. (2013). Steganography Based On
Random Pixel Selection for Efficient Data Hiding, International journal of
Computer Engineering and Technology (IJCET), ISSN 0976-6367, Vol. 4,
Issue 2, pp. 31-44. Cited by 4
Cited by
1. Qasim, M. A. and Pawar, D. (2013). Encryption & Steganography in IPv6 source
address” International Journal of Computer Engineering & Technology (IJCET),
ISSN: 0976 6375, Vol.4, No. 2 (2013) pp. 315-324.
2. Sumathi, C. P.; Santanam, T. and Umamaheswari, G. (2013). A Study of Various
Steganographic Techniques Used for Information Hiding." International Journal
of Computer Science & Engineering Survey (IJCSES), ISSN: 0976-2760, Vol.4,
no. 6 (2013), pp. 9-25.
3. Abdulhameed, Z. N. and Mahmood, M. K. (2014). High Capacity Steganography
Based on Chaos and Contourlet Transform for Hiding Multimedia Data.
International Journal of Electronics and Communication Engineering &
Technology (IJECET), ISSN: 0976-6472, Volume 5, Issue 1, (2014), pp. 26-42.
4. Mala, R. and Manimozi, I. A Novel Approach for Reversible Data Hiding in
Encrypted Images Using Key Based Pixel Selection. International Journal of
Computer Science & Engineering Technology (IJCSET), ISSN: 2229-3345, Vol. 5
No. 06 Jun 2014, pp. 715-719.
189
Appendix C
Participation in Conferences and
Workshops
1. “National conference on Current Trends in computer Science (CTCS 2010)”,
Department of Computer Science, Assam University, Silchar 22-24, February
2010, AUS.
2. “ISI-AU Workshop On Intelligent Data Analysis: Theory and Application”, 1-
5, March 2011, Computer Vision and Pattern Recognition Unit, Indian
Statistucal Institute, Kolkata and Department of Information Technology,
Assam University, Silchar, India.
3. “ISI-NEHU Winter School on Soft Computing, Pattern Recognition and
Image Processing”, October 20 – 24, 2011, Department of Information
Technology, North- Eastern Hill University, Shillong, Meghalaya.
4. “Workshop on Application of Mathematics in Computer Science and
Engineering”, August 2-4, 2011, Department of Mathematics, NIT, Silchar,
India.
5. “International Conference on Computer Science, Engineering and
Applications”, Organized by AIRCC, May, 26-27, 2012, New Delhi, India.
6. “13th
Workshop on Computational Information Processing”, December 3-7,
2012, Electronics and Communication Sciences Unit, ISI Kolkata and
Department of Information Technology, Assam University, Silchar, India.
7. “Workshop on Role of IPR in Electronics, Communication, Computing and
Devices”, November 27-28, 2013, Tezpur University IPR Cell and Institute of
Engineers, Silchar, India.