Design and Implementation of Lifting Based Wavelet and Adaptive LSB Steganography to Secret Data Sharing Through Image on FPGA

www.ijemr.net ISSN (ONLINE): 2250-0758, ISSN (PRINT): 2394-6962
112 Copyright © 2018. IJEMR. All Rights Reserved.
Volume-8, Issue-3, June 2018
International Journal of Engineering and Management Research
Page Number: 112-119
DOI: doi.org/10.31033/ijemr.8.3.14
Design and Implementation of Lifting Based Wavelet and Adaptive LSB
Steganography to Secret Data Sharing Through Image on FPGA
Kokila. B. Padeppagol1
and Sandhya Rani M H2
1
M. Tech in VLSI Design and Embedded Systems, Department of ECE, Sapthagiri College of Engineering, Bengaluru,
INDIA
2
Associate Professor & HOD, Department of ECE, Sapthagiri College of Engineering, Bengaluru, INDIA
1
Corresponding Author: kokilabp9@gmail.com
ABSTRACT
Image steganography is an art of hiding images
secretly within another image. There are several ways of
performing image steganography; one among them is the
spatial approach. The most popular spatial domain approach
of image steganography is the Least Significant Bit (LSB)
method, which hides the secret image pixel information in the
LSB of the cover image pixel information. In this paper a
LSB based steganography approach is used to design
hardware architecture for the Image steganography. The
Discrete Wavelet Transform (DWT) is used here to transform
the cover image into higher and lower wavelet coefficients
and use these coefficients in hiding the secret image. the
design also includes encryption of secret image data, to
provide a higher level of security to the secret image. The
steganography system involving the stegno module and a
decode module is designed here. The design was simulated,
synthesized and implemented on Artix -7 FPGA. The
operation hiding and retrieving images was successfully
verified through simulations.
Keywords--- LSB, Image Steganography, DWT,
Encryption and Decryption, Artix-7 FPGA
I. INTRODUCTION
Now a day, most of the data information is shared
over the internet in the form of text and the media content.
The internet is an open and wide channel for the
communication process and the data transfer through these
channels can be attacked by some stealing threat that
perform unauthorized accessing action over the data to
steal the information by unknown source. So,
confidentiality of these digital data information should be
preserve by adopting some security actions that protect the
digital data from getting stolen by unauthorized user. There
are some security approaches that include cryptography,
watermarking and steganographic concept which secures
data in such way that the original content of data cannot be
noticed to third party [1]. Cryptography is concept of
encryption and decryption that perform on digital image by
using secrete key to secure communication with following
property privacy Confidentiality, key exchange
authentication and non-repudiation. Encryption is a
technique that transforms plain data into encoded form
(cipher text) and decryption is a technique that is used to
get original data back from the encoded content [2].
Another approach for securing digital data is
Watermarking technique which is the process of
embedding some other content into original content of
data. Watermarking technique can be used for many
purposes like to hide data content, source tracking,
Broadcast tracking and copyright protection. [3]
Steganography technique is an art of hiding data
information in such way that original data is covered by
other media such as text, image, audio or video from the
and by this way it prevents from third party user.
Cover-object
Secret-data
Steganography Stego-object
Fig.1. Steganography concept
The above figure1.1 represents the general
process of Steganography technique where cover-object
and Secrete-data are uses to generate stegno-object.
Image Steganography concept is mainly
categorized into following techniques that are based on
spatial-domain and frequency-domain. In spatial method
data is hidden by modifying spatial characteristics of
image and the hidden data is in the form of image pixel
values. The spatial-domain consists two different methods
for hiding image data that are Least Significant Bit
technique (LSB) and Pixel Value Differencing technique
(PVD). In frequency-domain technique both the secret
message and carrier data are transformed into the
frequency domain by using different transformation
technique such as Discrete Fourier Transform (DFT),
Discrete Wavelet Transform (DWT), Discrete Cosine

Transform (DCT) for integrating the information into
image. At the decryption process the domain
transformation is need to extract the original content from
hidden data [4][5]. The FPGA hardware is widely used for
designing various steganographic techniques because
FPGA provides re-configuration feature and as well as the
faster and robustness application for the image processing.
In this paper a novel design for secret image data
sharing by using Adaptive LSB steganography is proposed.
A DWT using lifting method is used for Image
transformation and similarly, IDWT an inverse process
that is used at time of decryption process to recover the
original image. The proposed model is simulated and
implemented on the FPGA and lastly the presented model
is compared with existing models. The rest of the paper is
organized as- Section-2 provides brief review of existing
techniques. Section-3 presents the system design; Section-
4 presents implementation operation of proposed method
along with all details, Simulation results and performance
analysis is present in the Section-5 and finally, Section-6
reveals the conclusion and the future work.
II. REVIEW OF LITERATURE
The work carried out by Dahiya et al. [6] have
concentrated on the issue of multiplier fabrication on the
chip and presented a novel multiplier-less architecture for
DWT with using parallel flipping and shift adder technique
that reduces problem of critical path and provides efficient
implementation design and high-speed processing feature.
The outcome of study shows that the presented approach
achieves better performance in image and video
compression.
In the study of Rao et al. [7] have studied the
effect of image compression based on Haar wavelet and
DSP and found the increased processing speed of image
compression on digital processor when compared to
general processor. The outcome of the study delivers it can
be efficiently applicable on many image applications that
require high transmission processing such as satellite
images, HDTVs, medical images etc.
Lin et al. [8] have constructed a cell-based model
for that based on the Haar DWT to mitigate the problem of
imitated light-field image. The experimental result of this
study achieves an efficient performance in reconstructing
the blurry images with optimal bandwidth.
The work of Al-Afandy et al. [9] have offered a
secure framework for data hiding by using LSB technique
and cropping image steganography to enhance the security
of image data information. The experimental outcomes of
presented approach obtain optimal PSNR and CPU time
range that is sufficient to provide more security.
The work of Sugathan [10] has tried to improve
the quality features of Stego images by using LSB
replacement algorithm. The outcome of presented
approach achieves an improved image which is evaluated
by PSNR and MSE.
The work of Juarez-Sandoval et al. [11] have
studied some artifacts generated by the LSB matching
steganography and presented a steganalysis algorithm to
obtain compact feature vector.
Karthikeyan et al. [12] have focused on the data
security management and presents an innovative concept
by using data encryption algorithm. The objective of
author is to provide better security while the data is
transferring over the open communication channel.
Similarly, another approach for securing image
data information is given by the Abood [13] in which a
hybrid method of cryptography and Steganography is
applied to improve the security for end-to-end data
information.
In the same way another approach is proposed by
the Singh and Singh [14] that have discussed various
steganography techniques to provide strong security to
digital data which is shared over the internet channel.
III. SYSTEM BACKGROUND
In system domain, described different methods
which is used in Steganography Hardware (SH) model.
The fundamentals of the Least Significant Bit (LSB)
methodology of steganography, the lifting approach and
the chaotic scheme for cryptography is described.
(A) LSB Techniques
In image pixel LSB is the smallest bit in the byte
value. The LSB method changes some specific or more
bits in a cover picture. For a 24-bit picture, 3 bits may be
embedded and while in 8-bit picture one bit of secure
message is secreted. The binary representation of 135
decimal number, with the LSB tinted in below [1].
1 11 0 0 0 0 1
Fig.2. LSB technique
(B) LSB Insertion Method
The LSB technique of embedding secure message is
executed with the procedure which is shown in the figure
1. First of all, the embedded secret message is changed into
binary. Then the changed message along with cover image
file and stego key is proceed to the LSB encoder to achieve
the stego picture as the result.
SECRET
MESSAGE
ASCII TO
BINARY
LSB
ENCODER
STEGO
IMAGE
STEGO KEY
COVER
IMAGE FILE
Fig.3. Procedure for stego image through LSB encoder 15]

(C) Data embedding process
In data embedding system some input is taking
which is stego key, secret text and cover picture. To start
with the pixels of cover picture are extracted and after that
from secret text the characters are also extracted. Then
from stego key characters are extracted. After these
processes choosing the initial pixel and also chose the
characters from stego key and place in the initial
component of pixel. Next, show through end of key by
insertion a terminating symbol. Thereafter attach the text
character in first component of upcoming pixels by
exchanging it and it is in continue until the all characters
are embedded. Then insert a terminating symbol for
indication of text data end. And finally acquired stego
picture.
(D) Data Extraction from Stego Image
Figure 2 shows the secure data extraction by using
an LSB decoder. The LSB decoder uses stego key and
stego image as an input and extracts the secure data from
the stego picture.
STEGO
IMAGE
STEGO
KEY
LSB
DECODER
SECRET
MESSAGE
Fig.4. Data extraction using LSB decoder [1]
(E) Data Extraction Process
In data extraction procedure first of all the stego
image pixels are extracted, after that start from initial
pixels and extracted stego key symbols from starting
component of the pixels. If the key which is entered by the
receiver is correspond to the extracted key, then after move
to the next stage otherwise the program is terminated. In
next step the accurate key move to the next pixels and
extract the secure message characters from starting
component of next pixels. At last the secure message is
extracted from these whole processes.
(F) Lifting Approach based on Discrete Wavelet
Transform (DWT)
To implementation of DWT using lifting
approach. It is an effective way to achieve multi-stage
signal decomposition. The lifting method finds out the
maximum and minimum frequency values through Basic
Polynomial Interpolation (BPI).
Forward Wavelet Transform (FWT) contains
three basic steps:
i. Split: The split steps distribute the signal frame
into odd indexed and even indexed samples. The
indexed sample provides information about the
original signal recovery. [16]
ii. Predict: in the predict process the odd and even
indexed samples are interleaved. Interpolating
division is creating the predictor. To find the
difference between predict value and odd
samples, the wavelet coefficient is used.
iii. Update: the update process is essential to reduce
the memory requirements.
SPLIT
SEQUENCE
PREDICT ODD
VALUE
UPDATE STAGE
+
-
EVEN
SAMPLES
ODD
SAMPLES
µ0
δ-1,k
ɸ-1,k
Fig.5. FWT lifting steps [2]
IV. PROPOSED METHOD
Image stenography is the most significant
approach in the image processing field which hides the
secret information (i.e. image) within another image (i.e.
cover image). There are several methodologies which
performs the image stenography process. The most
adoptable approach is the spatial-domain based image-
stenography. In spatial approach, LSB (i.e. Least
Significant Bit) method is especially utilized for image
mapping operation that maps the secret image bits into 4
distinct LSBs of cover image.
The overall implementation process of proposed
Stegano-image module on FPGA is described as below.
A.Proposed Image-Steganography process
Generally, image steganographic is the method of
hiding secret image into another cover image. During this
process, image may be undergoing for encryption before
hiding it into cover image. The pictorial representation of
this process in given in the following figure: 6.
Figure: 6 Proposed Stegano Image Process

Figure 7. Decoding Operation
The system selects the (64 x 64) size of secret
image and embedding with (128 x 128) size of cover
image. Initially, the secret image will be encrypted by
EXORed encryption operation. Then cover image is
utilized and perform the lifting-based DWT operation
which results the lower and higher coefficients. Then
system embeds both encrypted secret image and cover
image by applying LSB insertion method. This phase
considers the two input images (i.e. encrypted secret image
and DWT covered image). The outcome of this phase is
proceeding for inverse DWT operation which obtains the
Stegano image.
Next, system considers the generated Stegano
image for image extraction process which is the opposite
process of embedding of secret image. At this phase,
Stegano image is filtered by DWT then secret image is
extracted via LSB decoding process. The decoded image
bits are further decoded by XORed based decryption
process and finally can get the secret image.
B.Interanal architectureof Proposed Steganography
module
An internal architectural view of proposed
steganographic module is shown in below figure:5.
Fig. 8. Internal design of proposed Stegano module
The proposed architecture is primarily composed
with five different blocks which are responsible to perform
steganographic operation. Such major blocks are; 1)
Encryption module, 2) Secret image splitter, 3) LSB
embedding, 4) DWT module and 5) Inverse DWT module.
Initially, both secret image and cover image data
are obtained from MATLAB tool which will convert those
images in terms of text-based data. The MATLAB
generated text-based data (i.e. 8bit secret image and 8bit
cover image data) is fed for encryption and results the
encrypted secret-image with secret encryption key. The
encrypted image further fed into image splitter and splits
the eightbits of image data into four parts (d1, d2, d3, and
d4). Parallelly, system filters the cover image through
DWT process and generates the “hout” (high pass) and
“lout” (low pass) coefficients for that cover image. Then
embedding module takes the d1, d2, d3, and d4 data and
hout data as input for LSB insertion operation which
inserts the d1, d2, d3, and d4 data into low-bits of hout
data. The inserted data results the output in terms of 8bits
dout signal. The dout and en_out signals are fed for inverse
DWT operation which generates the two coefficients (i.e.
hout and lout) of given image data.
While lifting based DWT module is designed
which contains the 8bits din signal (i.e. cover image), clk
and rst. The two 9-bits (hout and lout) output signals are
extracted from input image. However, Inverse DWT
module performs reverse operation of DWT module.
Generally, this module considers the two input components
(i.e. higher and lower frequency) and generates the two
9bits spatial domain signals.
V. RESULTS AND ANALYSIS

The tonsillitis detection system using Gabor
filtering approach presented here was modeled using
Verilog simulated and synthesized. The Xilinx 14.7 ISE
was used here for system modeling. The simulations for
the designs were carried out using Modelsim6.3f. The
synthesis and the final implementation of the system
design was performed on Artix 7 FPGA Board Device
7A100T-3 CSG324.
Main steganography module
The figure 9 depicts the top module design
synthesized result which shows the input port and output
port signals as defined in the design.
Fig. 9. Main steganography top Module
The Main stegno module simulation results as digital waveforms are displayed in figure 9.
Fig.10. simulation waveforms for main stegano module
The waveforms show the cover image and secret
image data input as 8-bit pixel data. The key input for the
encryption of secret image data is selected as “01101100”.
The two outputs of the module are the odd and even
components of the stego image. The secret image data
shown in the waveform is “00110110”.
The stego image data obtained from main stegno
module simulation was used in constructing the stego
image. The stego mage constructed is shown in figure 11.

Fig.11. Main steganography Results-(a) cover image, (b) secret image (c) stego image
MAIN DECODING The synthesized result of the top-level design for
the main decoding module is shown in figure 12.
Fig.12. Main decoding top Module
The waveform results for the main_decoding top
module are shown in figure 12.
Fig.13. Main decoding Simulation Results
The data out obtained by simulating the
main_decoding module was “01101100” which matches
with the secret image data input to the main_stegno
module. The images obtained by simulating the main
decoding module are shown in figure 14.

(a) (b)
Fig.14. Main decoding Results-(a) stegno image (b) hidden image
VI. COMPARISON RESULTS A comparison of the designed stegno module and
decoding module resource utilization with the existing
designs of [] are shown in table 1.
Table 1: Resource utilization Comparison
VII. CONCLUSION
An image steganography system was designed
here using DWT-LSB based secret image insertion and
extraction. The steganography system designed here
applies encryption procedure to the secret image data
before hiding the secret image inside the cover image. The
DWT process is applied on the cover image to split the
images as higher and lower coefficients. The coefficients
were utilized to hide he secret image. The main decoder
module of the steganography system was designed here to
extract the encrypted hidden image data and decrypt the
data to get back the secret image.
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Design and Implementation of Lifting Based Wavelet and Adaptive LSB Steganography to Secret Data Sharing Through Image on FPGA

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