Embedding a Large Amount of Information Using High Secure Neural Based Steganography Algorithm
In this paper, we construct and implement a new
Steganography algorithm based on learning system to hide a large
amount of information into color BMP image. We have used adaptive
image filtering and adaptive non-uniform image segmentation with
bits replacement on the appropriate pixels. These pixels are selected
randomly rather than sequentially by using new concept defined by
main cases with sub cases for each byte in one pixel. According to
the steps of design, we have been concluded 16 main cases with their
sub cases that covere all aspects of the input information into color
bitmap image. High security layers have been proposed through four
layers of security to make it difficult to break the encryption of the
input information and confuse steganalysis too. Learning system has
been introduces at the fourth layer of security through neural
network. This layer is used to increase the difficulties of the statistical
attacks. Our results against statistical and visual attacks are discussed
before and after using the learning system and we make comparison
with the previous Steganography algorithm. We show that our
algorithm can embed efficiently a large amount of information that
has been reached to 75% of the image size (replace 18 bits for each
pixel as a maximum) with high quality of the output.
[1] S-Tools (http://digitalforensics. champlain. edu/ download/ s-tools
4.zip).
[2] Chandramouli, R. and N. Memon,. "Analysis of LSB based image
steganography techniques," Proc. of ICIP, Thessaloniki, Greece, pp. 7-
10, Oct. 2001.
[3] Dumitrescu, S., W. Xiaolin and Z. Wang, "Detection of LSB
steganography via sample pair analysis," In: LNCS, Springer-Verlag,
New York, Vol. 2578/2003, pp: 355-372, 2003.
[4] Ahn, L.V. and N.J. Hopper, "Public-key steganography. In Lecture
Notes in Computer Science of Advances in Cryptology,"
EUROCRYPT 2004, Vol. 3027 / 2004, Springer-Verlag Heidelberg, pp:
323-341, 2004.
[5] Pang, H.H., K.L. Tan and X. Zhou, "Steganographic schemes for file
system and b-tree," IEEE Trans. on Knowledge and Data Engineering,
Vol. 16, pp.701-713, 2004.
[6] Dobsicek, M., "Extended steganographic system," In: 8th Intl. Student
Conf. on Electrical Engineering. FEE CTU. 2004
[7] Mittal, U. and N. Phamdo, "Hybrid digital-analog joint source-channel
codes for broadcasting and robust communications," IEEE Trans. on
Info. Theory, vol. 48, pp. 1082 -1102, 2002.
[8] Pavan, S., S. Gangadharpalli and V. Sridhar, "Multivariate entropy
detector based hybrid image registration algorithm," IEEE Intl. Conf. on
Acoustics, Speech and Signal Processing, pp: 18-23, 2005.
[9] Moulin, P. and J.A. O-Sullivan, "Information-theoretic analysis of
information hiding," IEEE Trans. on Info. Theory, vol. 49, pp. 563-
593, 2003.
[10] Amin, P., N. Liu and K. Subbalakshmi, "Statistically secure digital
image data hiding," IEEE Multimedia Signal Processing MMSP05,
China, 2005.
[11] Jackson, J., G. Gunsch, R. Claypoole and G. Lamont,. "Detecting novel
steganography with an anomaly- based strategy," J. Electr. Imag., Vol.
13, 860- 870, 2004.
[12] Nameer N. EL-Emam, "Reallocation of mesh points in fluid problems
using back-propagation algorithm," Information Journal, Vol 9, No. 1,
pp 175-184. January 2006.
[13] C. Zhang, H.W. Guesgen, W.K. Yeap "Neural Based Steganography,
Lecture note in computer science Computational Intelligence. Neural
Networks," LNAI 3157, pp. 429-435, Springer-Verlag Berlin
Heidelberg 2004.
[1] S-Tools (http://digitalforensics. champlain. edu/ download/ s-tools
4.zip).
[2] Chandramouli, R. and N. Memon,. "Analysis of LSB based image
steganography techniques," Proc. of ICIP, Thessaloniki, Greece, pp. 7-
10, Oct. 2001.
[3] Dumitrescu, S., W. Xiaolin and Z. Wang, "Detection of LSB
steganography via sample pair analysis," In: LNCS, Springer-Verlag,
New York, Vol. 2578/2003, pp: 355-372, 2003.
[4] Ahn, L.V. and N.J. Hopper, "Public-key steganography. In Lecture
Notes in Computer Science of Advances in Cryptology,"
EUROCRYPT 2004, Vol. 3027 / 2004, Springer-Verlag Heidelberg, pp:
323-341, 2004.
[5] Pang, H.H., K.L. Tan and X. Zhou, "Steganographic schemes for file
system and b-tree," IEEE Trans. on Knowledge and Data Engineering,
Vol. 16, pp.701-713, 2004.
[6] Dobsicek, M., "Extended steganographic system," In: 8th Intl. Student
Conf. on Electrical Engineering. FEE CTU. 2004
[7] Mittal, U. and N. Phamdo, "Hybrid digital-analog joint source-channel
codes for broadcasting and robust communications," IEEE Trans. on
Info. Theory, vol. 48, pp. 1082 -1102, 2002.
[8] Pavan, S., S. Gangadharpalli and V. Sridhar, "Multivariate entropy
detector based hybrid image registration algorithm," IEEE Intl. Conf. on
Acoustics, Speech and Signal Processing, pp: 18-23, 2005.
[9] Moulin, P. and J.A. O-Sullivan, "Information-theoretic analysis of
information hiding," IEEE Trans. on Info. Theory, vol. 49, pp. 563-
593, 2003.
[10] Amin, P., N. Liu and K. Subbalakshmi, "Statistically secure digital
image data hiding," IEEE Multimedia Signal Processing MMSP05,
China, 2005.
[11] Jackson, J., G. Gunsch, R. Claypoole and G. Lamont,. "Detecting novel
steganography with an anomaly- based strategy," J. Electr. Imag., Vol.
13, 860- 870, 2004.
[12] Nameer N. EL-Emam, "Reallocation of mesh points in fluid problems
using back-propagation algorithm," Information Journal, Vol 9, No. 1,
pp 175-184. January 2006.
[13] C. Zhang, H.W. Guesgen, W.K. Yeap "Neural Based Steganography,
Lecture note in computer science Computational Intelligence. Neural
Networks," LNAI 3157, pp. 429-435, Springer-Verlag Berlin
Heidelberg 2004.
@article{"International Journal of Information, Control and Computer Sciences:55144", author = "Nameer N. EL-Emam", title = "Embedding a Large Amount of Information Using High Secure Neural Based Steganography Algorithm", abstract = "In this paper, we construct and implement a new
Steganography algorithm based on learning system to hide a large
amount of information into color BMP image. We have used adaptive
image filtering and adaptive non-uniform image segmentation with
bits replacement on the appropriate pixels. These pixels are selected
randomly rather than sequentially by using new concept defined by
main cases with sub cases for each byte in one pixel. According to
the steps of design, we have been concluded 16 main cases with their
sub cases that covere all aspects of the input information into color
bitmap image. High security layers have been proposed through four
layers of security to make it difficult to break the encryption of the
input information and confuse steganalysis too. Learning system has
been introduces at the fourth layer of security through neural
network. This layer is used to increase the difficulties of the statistical
attacks. Our results against statistical and visual attacks are discussed
before and after using the learning system and we make comparison
with the previous Steganography algorithm. We show that our
algorithm can embed efficiently a large amount of information that
has been reached to 75% of the image size (replace 18 bits for each
pixel as a maximum) with high quality of the output.", keywords = "Adaptive image segmentation, hiding with high
capacity, hiding with high security, neural networks, Steganography.", volume = "2", number = "11", pages = "3762-12", }