IRJET- High Capacity Reversible Data Hiding in Encrypted Images by MSB Prediction Method

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 3844
HIGH CAPACITY REVERSIBLE DATA HIDING IN ENCRYPTED IMAGES BY
MSB PREDICTION METHOD
Aswathy Lekshmi. S1, Hari. S2, Netha Merin Mathew3
1PG Student, Dept. Of Electronics and Communication Engineering, Mount Zion College of Engineering, Kerala,
India
2Assistant Professor, Dept. Of Electronics and Communication Engineering, Mount Zion College of Engineering,
Kerala, India
32Assistant Professor, Dept. Of Electronics and Communication Engineering, Mount Zion College of Engineering,
Kerala, India
----------------------------------------------------------------------***---------------------------------------------------------------------
Abstract -Data Privacy hasreceivedconsiderableattention
in all fields. In the last few years, data and visual privacy has
become a major problem. Because of this, Reversible Data
Hiding (RDH) in encrypted image has a lot of attention from
the communities of privacy, security and protection. The
previous RDH methods are not able to hide a large amount of
information with very high embedding capacity as well as it
does not provide a very good reconstructedimagequality. This
paper presents a high efficient reversible data hiding in
encrypted image by MSB prediction method. In this method
RDH is a process to embedded useful data into a cover media
or encrypted image using MSB prediction. It is a two step data
hiding procedure, first embed data inside a media file and
second, the hidden data and the original image can be loss
lessly extracted. Here present two approaches: (1) High
Capacity Reversible Data Hiding on MSBs with Correction of
Prediction Error (CPE – MHCRDH), (2) High Capacity
Reversible Data Hiding on MSBs with Embedded Prediction
Errors (EPE – MHCRDH). Before transmitting the data
embedded image to the receiver we can use image
compression techniques toreducetheamountofdatarequired
to represent an image. With these methods, the results are
better than those obtained in the previous method.
Key Words: Reversible Data Hiding (RDH), Embedding
Capacity, Compression, MHCRDH, MSB Prediction.
1. INTRODUCTION
Communication of digital information becomes
frequent nowadays, because of its fast access capability. A
wide range of technologies for end-to-end protection are
needed to resist the security threats in modern
communication. Two main techniques for secure
communication is Data hiding and Cryptography. In
cryptography, the plain data is changed into an unreadable
form called cipher data. The limitation of cryptography is
that the third party is always conscious about the
communication. In data hiding, the data is hidden in a cover
file and it will be transmitted over the network [2] - [6].
Hiding the existence of secret information is the main
advantage of data hiding technique over cryptography.
Therefore, hacker or eavesdropper, and other, does not
access the original message or any other type of information
transmitted through the public networks such as internet.
Data hiding is a process to embed useful data
(information) into a cover media. RDH is a data
hiding techniques. Reversible Data Hiding can be defined as
an approach where data is hidden in the cover media that
may be an image. It is a two-step data hiding procedure:first,
embed (hide) data inside a media file and second, the
hidden data and the original image can be loss lessly
extracted. A reversible data hiding is an approach,whichcan
recover the original image loss lessly after the data have
been extracted from the cover image.
This paperintroducesaneffectiveapproachforData
Hiding in Encrypted domain. We suggest embedding the
secret message by two bit MSB substitution. As the values of
the replaced MSB are lost during the data hiding phase, it is
necessary to be able to predict them without errors during
the decoding phase. In our design uses two methods for
reversible data hiding, i.e., CPE – MHCRDH and EPE –
MHCRDH.
The first CPE – MHCRDH (High Capacity Reversible
Data Hiding on MSB by Correction of Prediction Errors)
method consist of correcting all the prediction errors before
encryption. At the decryption phase, the original image can
be reconstructed perfectly but secret message has some
distortions but where we are able toembedtwobitperpixel.
In the second method EPE – MHCRDH (High Capacity
Reversible Data Hiding on MSB by Embedded Prediction
Errors) consist of the original image is directly encrypted,
but after the encryption step, the location of the prediction
errors is embedded (EPE). During the data hiding phase, in
both approaches, the MSB of each available pixel is
substituted in the encrypted image by two bit of the secret
message. After that the data hided image of both methods is
compressed using loss lees LZ77 compression techniques.
LZ77 compression works by finding sequence of data that
are repeated. At the end of the process, the compressed
image can be decompressed and the embedded data can be
extracted without any errors and the clear image can be
reconstructed loss lessly by using MSB prediction. The

proposed scheme provides a good security level and can be
used to preserve the original image content confidentiality,
while offering authenticity or integrity. It improves the
reconstructed image quality as well as the embedding
capacity.
The rest of the paper is organizedasfollows.Section
II gives an overview of related work on reversible data
hiding in encrypted images. Then, the proposed method is
described in detail in Section III. Experimental results and
analysis are provided in Section IV. Finally, the conclusion is
drawn in Section V.
2. RELATED WORK
Reversible data hiding(RDH)isparticularlysuitable
for authentication and data enrichment. It consists of
embedding a hidden message into an image. After that, it is
possible to extract the secret data and to loss lessly recover
the original image. The methods are based on lossless
compression appending, difference expansion [10], [11],
histogram shifting [12], [13], [9] or a combination of these
schemes [8], [15]. Also, by altering the content of an original
image, encryption provides a particular visual
confidentiality. Cryptosystems can be divided into two
groups.
In [2], Pauline Puteauxand WilliamPuechproposea
reversible data hiding in encrypted images based on
adaptive local entropy method. The original image is firstly
encrypted with AES cryptosystem in ECB mode to ensure
image content confidentiality. In fact, we suggest adapting
the Shannon entropy measurement in order to be able to
perform a significant local analysis. During the
reconstruction phase, weuse thisstatistical metric insteadof
standard deviation to loss lessly recover each block of 4 × 4
pixels from the original image. In this approach, thepayload
is quite small (0.0625 bpp).
In [3], this paper proposes a novel scheme of
reversible data hiding (RDH) in encrypted images using
distributed source coding (DSC). After the original image is
encrypted using a stream cipher by the content owner, the
data - hider compresses a series of bits selected from the
encrypted image to make a room for the secret data. The bit
selected from encrypted image is Slepian - Wolf encoded
using low density parity check (LDPC)codes.Onthereceiver
side, the secret bits can be extracted if the receiver has the
embedding key only. In case were the receiver has only the
encryption key, they can recover the original image using an
image estimation algorithm. If the receiver has boththedata
hiding and encryption keys, they can recover both secret
data and the original image using the distributed source
decoding.
Wien Hong, Tung-Shou Chen, and Han-Yan Wu in
[4], this letter proposes an improved version of Zhang’s
reversible data hiding method in encrypted images. The
work divides the encrypted image into blocks, and each
block carries one bit by replacing three LSBs. The data
extraction and image recovery can be achieved by analyzing
the block smoothness. Zhang’s work is not fully utilizing the
pixels for calculating thesmoothnessofeachblock.Alsodoes
not consider the pixel correlations in the border of
neighboring blocks. These two issuesreducethecorrectness
of the data extraction. This paper gives a better scheme for
measuring the smoothness of the blocks, and uses the side-
match techniques to decrease the error rate of extracted-
bits.
In [5], Zhang et al. encrypted the cover image by using
public key cryptography with probabilistic and
homomorphic properties. After the encryption phase, they
embed data in the LSB planes of the encrypted pixels.During
the decoding phase, as the introduced distortion was quite
low, the embedded data is extracted and the original image
was recovered loss lessly.
Chao-Yung Hsu, Chun-Shien Lu, and Soo-Chang Pei
propose a privacy-preserving realizationoftheSIFTmethod
based on homomorphic encryption. Security analysis is
based on the discrete logarithm problem and RSA that
PPSIFT is secure against cipher text attack only and known
plaintext attack. Experimental results of proposed
homomorphic encryption-based privacy-preserving SIFT
obtained from different cases gives comparably to the
original SIFT. Also this method is useful in SIFT-based
privacy-preserving applications [6].
Ma et al. were the first to describea RRBEtechnique
[7]. They proposed to release a part of the original image by
applying a RDH method ofhistogramshifting.Afterthat,they
encrypted the image and then inserted information by
substituting some LSB values in the encrypted image. With
this method, the payload is higher than in previous methods
but the reconstructed image is altered when compared with
the original.
In [8], prediction-error expansion (PEE) based
reversible data hiding gives better exploiting image
redundancy usually provides a superiorperformance.Inthis
paper, an efficient RDH scheme based on pair wise PEE is
proposed. The pair wise PEE is a novel reversible mapping
that utilizes the correlations among prediction- errors. With
the help of this type of correlations, the distortion can be
controlled at a low level, and thereby the proposed scheme
outperforms some state-of-the-art RDH algorithms.
2. PROPOSED SYSTEM
The previous method hide 1 bit per pixel in theMSB.
It does not have high embedding capacity and visual quality.
Also, the digital image usually requires large number of bits;
cause criticalproblemfordigitalimagedatatransmissionand

storage. For these reason, Here present a new high capacity
reversible data hiding scheme for encrypted images based
on MSB (two most significant bits) prediction with a very
highcapacity. Also wecanuseimagecompressiontechniques
to reduce the amount of data required to represent an image
and reducing the bandwidth required to transmit it.
We presenttwoapproaches,theseare:highcapacity
reversible data hiding on MSBs with correctionofprediction
errors (CPE-MHCRDH) and high capacity reversible data
hiding on MSBs with embedded prediction errors (EPE-
MHCRDH). In CPE-MHCRDH, it is possible to embed a secret
message in the MSB of the encrypted image. At the decoding
phase, the original image is reconstructed perfectly, but the
secret message becomes distorted. In EPE – MHCRDH
method, it is possible to embed a secret message in the
MSB of the encrypted image.Atthedecodingphase,the
original image becomes distorted, but the secret
message can be reconstructed perfectly.
2.1 CPE-MHCRDH
In the CPE-MHCRDH approach, as shown in Fig1.In
this method, we propose to embed the secret message (2 bit
per pixel) by MSB substitution with very high embedding
capacity and visual quality. The goal of our proposition is
that an original image I, could be encrypted by using a secret
key Ke and that another person could embed a message by
using a data hiding key Kw, without knowing Ke [1].
Fig - 1: Block diagram of CPE-MHCRDH Method
The encoding phase consists of four steps: the prediction
error detection, the image encryption, the 2 bit data hiding
by MSB substitution and compression as shown in fig 1.
1. Prediction error detection:
In this method, the input image is taken from the
data base. First predict all the prediction errors in the input
image i.e., we can embed the secret message (2 bit per pixel)
by MSB substitution, the original MSB values are lost after
the data hiding step. It is important, during the decoding
phase, to be able to predict them without any errors.Indeed,
in order to reconstruct the original image, weproposetouse
the previous pixels to predict the current pixel value [1].
 Consider the current pixel P(i, j), with 0 ≤ i < m and
0 ≤ j < n, and its inverse value, which is inv(i, j) =
(P(i, j) + 128) mod 256.
 From the previously scanned neighbors of P(i, j),
compute the value Perror (i, j).
(1)
 Calculate the absolute difference between Perror(i, j)
and P(i, j) and its inv(i, j).
(2)
 Compare the values of ∆ and ∆inv. If ∆ < ∆’, there is
no prediction error.
 After the prediction error detection phase, we
propose to pre-process the original image Iinorder
to obtain an image I’ without any prediction errors.
(3)
2. Image Encryption:
In order to make theoriginal imageIunreadable,we
encrypt it by using an encryption keyKe.Theelementsofthis
key are used as parameters of a chaotic generator, based on
the Piecewise Linear Chaotic Map (PWLCM) [14]. By using
this chaotic generator, a sequence of pseudo-random bytes
S(i, j) is obtained and the encrypted pixels Pe(i, j) can be
calculated through exclusive-or (XOR) operation:
(4)
3. Data embedding :
In the data embedding phase, itispossibleto embed
data in the encrypted image without knowing either the
encryption key Ke used during the previous step. By using
the data hiding key Kw, the to-be-inserted message is first
encrypted in order to prevent its detection after embedding
in the marked encrypted image. Next,pixelsofthe encrypted
image are scanned from left to right, thenfromtoptobottom

and the MSB of each available pixel is substituted by two bit
bT, with 0 ≤ T< m × n, of the secret message:
(5)
Note that only the first pixel cannot be marked because its
value is not predictable, thus its value must not be changed.
4. Compression:
After this process, we can compress the data
embedded image using image compression techniques.
Image compression is reducing the size without degrading
the quality of the image to an unacceptable level. Here we
use LZ77 compression technique. LZ77 compression works
by finding sequence of data that are repeated. The sequence
of characters to be compressed is identical to the characters
found within the sliding window.Thesequenceofcharacters
is replaced by two numbers: a distance is representing how
far back into the window the sequence starts,anda length,is
representing the number of characters for which the
sequence is identical.
5. Data extraction and image recovery
For the decoding phase, since ourmethodisseparable,
we can extract the secret message and reconstruct the clear
image IR separately. Before that we can decompressed the
data embedded image. If the recipient has a key Kw, the
pixels from the decompressed image are scannedinthescan
line order and the MSB of each pixel are extractedinorderto
retrieve the encrypted secret message:
(6)
If the recipient only has Ke, the image IR can be
reconstructed and the secret data can be extracted by using
the following steps:
 The encryption key Ke is used to generate the
sequence S (i, j), with m × n pseudo-random bytes.
 The pixels of the decrypted image are scanned in
the scan line order, and for each pixel,thesevenLSB
are retrieved by XORing the decrypted value Pdata(i,
j) with the associated binary sequence S(i, j) in the
pseudo-random stream:
(7)
The MSB value is predicted:
 With the values of the previously decrypted
adjacent pixels, the value of the predictor Perror(i, j)
is computed.
 The pixel value is considered with MSB =0andwith
MSB = 1 and the differences between each of these
two values and Perror(i, j) are calculated. These
values are recorded as ∆0 and ∆1.
(8)
 The smallest value between ∆0 and ∆1 gives the
searched pixel value:
(9)
2.2 EPE – MHCRDH
In this method the original image is directly
encrypted, but after the encryption step, the location of the
prediction errors is embedded (EPE). During thedata hiding
phase, in both approaches, the MSB of each available pixel is
substituted in the encrypted image by 2 bit of the secret
message that embedded image can be loss lessly
compressed.
Fig - 2: Block diagram of EPE-MHCRDH Method
At the end of the process, the data embedded image
can decompressed and the embedded data can be extracted
without any errors and the clear image can be reconstructed
loss lessly by using MSB prediction as shown in fig 2. The
MSB of each available pixel is substituted by 2 bit of the
secret message as same as the previous CPE–MHCRDH
method. The difference between the two methods is that in
CPE – MHCRDH method correcting the prediction errors

(CPE) before encryption and in EPE – MHCRDH method the
location of the prediction errors is embedded (EPE) [1].
1. Embedding of the error location binary map:
During prediction error detection [1], the location of
the prediction errors is stored in the error location binary
map. Then, the original image I is encrypted by using Eq.(4).
Before the embedding step, the encrypted image Pe is
adapted to avoid prediction errors. The encrypted image Pe
is then divided into blocks of eight pixels and scanned, block
by block, in the scan line order. If at least one prediction
error is identified in a block according to the error location
binary map, the current block is surrounded by two flags by
replacing the MSB of each pixel in the previous and the
following blocks by 1. If there are errors in two adjacent
blocks, the flag which indicates theendoftheerrorsequence
is shifted until the next block without error as indicated in
Fig. 3. In the case where there is no error inthecurrentblock
and if it does not serve as a flag, then the eight pixels of this
block are used for data hiding i.e., the available block with 0
represent the block is not used for data hiding if there is 1
represent the block is used for data hiding.
Fig - 3: Finding available block for data hiding
2. Data extraction and image recovery:
In this phase, three cases are considered: (1) the
recipient has only the data hiding key, (2) the recipient has
only the encryption key and (3) the recipient has both the
encryption and the watermarking keys. In the first case, the
recipient can extract the secret message by following these
steps
 The pixels of the decompressed image are scanned
in the scan line order and for each pixel; the MSB
value is extracted, according to Eq. (6), and stored.
Before the first sequence of eight MSB equal to 1,
the extracted values are bits of the embedded
message.
 When such a sequence is encountered, it indicates
the beginning of an error sequence: the next pixels
were not marked during the data hiding step. So,
scan pixels until the next sequence of eight MSB
equal to 1, which indicates the end of the error
sequence.
 Repeat this process until the end of the image.
 Finally, use the data hiding key to obtain the clear
text of the secret message
In the second case, the recipient can reconstruct the original
image I by using MSB prediction:
 Use the encryption key to generate the pseudo-
random chaotic sequence.
 Scan the pixels of the decompressed image in the
Scan line order and for eachpixel,retrievetheseven
least significant bits (LSB) of P(i, j)byXORingPdata(i,
j) with binary sequence in the chaotic stream.
Predict the MSB value
 Consider P(i, j)MSB=0 and P(i, j)MSB=1 as thepixel value
with MSB = 0 and MSB = 1, respectively.
 Calculate the absolute difference between each of
these two values with P(i, j −1) and with P(i−1, j) is
Perror(i, j). These values are recorded as ∆0 and ∆1
using Eq. 8.
The smaller value gives the original pixel value by using
the Eq. 9. Finally, if the receiver has both the data hiding and
encryption keys, they can extract the secret message and
reconstruct the original image.
3. RESULT
For data hiding methods in encrypted images, we
have to measure different performances: embedding
Capacity, PSNR, SSIM and Compression rate. We applied our
method on different 512 x 512 images like Baboon, Barbara,
Boat, Hill, House, Lena and BOWS-2 database [1]. We first
applied our two approaches on the Lena 512 × 512 pixels,
illustrated in Fig. 3. Fig. 4 shows the results obtained with
the CPE-MHCRDH approach and Fig. 5, with the EPE-
MHCRDH approach. In Fig. 9.a and Fig. 10.a, in white, we can
see the location of all the pixels with prediction errors.

Fig - 3: Original image I
(a) (b)
(c) (d)
(e)
Fig - 4: Illustration of our proposed CPE – MHCRDH
method on the test image Barbara (512 x 512), (a) Binary
error location map with predicted error, (b) Pre-processed
image I’ (Error corrected image), (c) Encrypted image Pe(i,
j), (d) Data embedded image (2 bit per pixel) with
1.999985 capacity, (e) Recovered image, PSNR=44.699724
dB, SSIM= 0.996905
In the CPE-HCRDH approach (Fig. 4.a), they are
pixels of the original image whose the MSB would be badly
predicted if we do not adapt their values during the pre-
processing phase. After thispre-processingstep,theadapted
images are obtained. This is similar to original image. The
compression reduces the number of bits required to
represent an image. The size before compression is 524288
and the size after compression is 89570. Therefore the
percentage of compression is 82%. At the decoding phase
the compressed image can bedecompressedthenextractthe
data and the image. Finally, after data extraction,
reconstructed images are exactly the same as pre-processed
images and very similar to original images, even when some
pixel values were changed during the preprocessing step. In
these cases, PSNR is high and SSIM is close to 1: PSNR =
44.699 dB, SSIM = 0.9969 and PSNR and SSIM for other
image are similar with this result.
(a) (b)
(c) (d)
Fig - 5: Illustration of our proposed EPE – MHCRDH
method on the test image Barbara (512 x 512), (a) Binary
error location map with predicted error, (b) Encrypted
image Pe(i, j), (d) Data embedded image (2 bit per pixel)
with 1.999985 capacity, (e) Recovered image,
PSNR=43.999652 dB, SSIM= 0.995837
In the EPE-HCRDH approach(Fig. 5.a);theyindicateall
the pixels which will not be marked. Indeed, in addition, in
grey we show the pixels which are not used to embed bits of
the secret message because they serve as flags or are part of
an error sequence. Note that the prediction errors are often
on the edges and there is sometimes more than one error in
the same block and, in these cases, the loss in terms of
embedding capacity decreases.
Note that the prediction errors are often on the
contours and there is sometimes more than one error in the
same block. Moreover, if there are errors in two adjacent
blocks, the flag which indicates theendoftheerrorsequence
is shifted. In these two cases, the loss of embedding capacity
is then less important. Fig. 4.c and Fig. 5.b are the encrypted
images. Fig. 4.a and Fig. 5.a are the highlighted prediction
errors: the initial information and the errorslocationare not
visible anymore. Fig. 4.d and Fig. 5.c present the data
embedded encrypted images obtained in the final stepofthe
encoding. Thendata embeddedimageiscompressedassame
in the CPE – MHCRDH method. In Fig. 4.e and Fig. 5.d, note
that the reconstructed images are exactly the same as the
original ones: all pixels are correctly reconstructed. To
conclude, our method proposes a very good tradeoff
between the embedding capacity and the reconstructed
image quality: it is possible to hide a large amount of data in
an encrypted image and to recover perfectly the original
image after data extraction.

4. CONCLUSION
In this paper, we propose a high capacity reversible
data hiding method in encrypted images based on the MSB
prediction. Indeed, by replacing all the MSBintheimage,itis
possible to hide two bit per pixel. In additiontothisexcellent
embedding capacity, the reconstructedimagequalityishigh.
By analyzing the content of the original image, all the
prediction errors are highlighted and the encryptedimageis
modified accordingly. After that, by substituting most of the
MSB values in the image, it is possible to hide a large
message and compression reduces the number ofbitused to
represent an image. There for we can reduce the bandwidth
required to transmit and receive an image. Finally, in the
extraction phase, the compressed image is decompressed
and the original image is reconstructed without any errors
and the secret message is perfectly extracted
ACKNOWLEDGEMENT
We thank the anonymous reviewers and the editors for
the thorough reviews and comments. Also thanks the Head
of Electronics and communication Department Professor
Rangit Varghese and the M.Tech co-coordinator Asst. Prof.
Shahana Habeeb Mohammed. Express our sincere gratitude
internal supervisor, Asst Prof. Hari S for rendering us all the
facilities for the completion of the project. Also like to
express our thanks to all other faculty members of
Electronics and Communication Department at Mount Zion
College of Engineering, Kadammanitta for the valuable help
provided by them.
REFERENCES
[1] Pauline Puteaux, and William Puech, “An Efficient MSB
Prediction-Based Method for High-Capacity Reversible
Data Hiding in Encrypted Images” IEEE Transactionson
Information Forensics and Security, 2018.
[2] Pauline Puteaux and William Puech “ Reversible Data
Hiding in Encrypted Images based on Adaptive Local
Entropy Analysis” IEEE, 2017
[3] Z. Qian and X. Zhang, “Reversible data hiding in
encrypted images with distributed source encoding,”
IEEE Transactions on Circuits and Systems for Video
Technology, vol. 26, no. 4, pp. 636–646, 2016.
[4] W. Hong, T.-S. Chen, and H.-Y. Wu, “An improved
reversible data hiding in encrypted images using side
match,” IEEE Signal Processing Letters, vol. 19, no.4,pp.
199–202, 2012.
[5] X. Zhang, J. Long, Z. Wang, and H. Cheng, “Lossless and
reversible data hiding in encrypted images with public-
key cryptography,” IEEE Transactions on Circuits and
Systems for Video Technology, vol. 26, no. 9, pp. 1622–
1631, 2016.
[6] C.-Y. Hsu, C.-S. Lu, and S.-C. Pei, “Image feature
extraction in encrypteddomainwithprivacy-preserving
SIFT,” IEEE Transactions on Image Processing, vol. 21,
no. 11, pp. 4593–4607, 2012.
[7] K. Ma, W. Zhang, X. Zhao, N. Yu, and F. Li, “Reversible
data hiding in encrypted images by reserving room
before encryption,” IEEE Transactions on Information
Forensics and Security, vol. 8, no. 3, pp. 553–562, 2013
[8] B. Ou, X. Li, Y. Zhao, R. Ni, and Y.-Q. Shi, “Pairwise
prediction-error expansion for efficient reversible data
hiding,” IEEE Transactions on Image Processing,vol.22,
no. 12, pp. 5010–5021, 2013
[9] W.-L. Tai, C.-M. Yeh, and C.-C. Chang, “Reversible data
hiding based on histogram modification of pixel
differences,” IEEE Transactions on Circuits andSystems
for Video Technologyy, vol. 19, no. 6, pp. 906–910, 2009
[10]J. Tian, “Reversible watermarking by difference
expansion,” in Proceedings of Workshop on Multimedia
and Security, vol.19,2002.
[11] ——, “Reversible data embedding using a difference
expansion,” IEEE Transactions on Circuits and Systems
for Video Technology, vol. 13, no. 8, pp. 890–896, 2003.
[12] X. Gao, L. An, Y. Yuan, D. Tao, and X. Li, “Lossless data
embedding using generalized statistical quantity
histogram,” IEEE Transactions on Circuits and Systems
for Video Technology, vol. 21, no. 8, pp. 1061– 1070,
2011.
[13] Z. Ni, Y.-Q. Shi, N. Ansari, and W. Su, “Reversible data
hiding,” IEEE Transactions on Circuits and Systems for
Video Technology, vol. 16, no. 3, pp. 354–362, 2006.
[14] S. Li, G. Chen, and X. Mou, “On the dynamical
degradation of digital piecewise linear chaotic maps,”
International Journal of Bifurcation and Chaos, vol. 15,
no. 10, pp. 3119–3151,2005.
[15] V. Sachnev, H. J. Kim, J. Nam, S. Suresh, and Y. Q. Shi,
“Reversible watermarking algorithm using sorting and
prediction,” IEEE Transactions on Circuits and Systems
for Video Technology, vol. 19, no. 7, pp. 989–999, 2009.

IRJET- High Capacity Reversible Data Hiding in Encrypted Images by MSB Prediction Method

More Related Content

What's hot (19)

Similar to IRJET- High Capacity Reversible Data Hiding in Encrypted Images by MSB Prediction Method (20)

More from IRJET Journal (20)

Recently uploaded (20)

IRJET- High Capacity Reversible Data Hiding in Encrypted Images by MSB Prediction Method