Covert Image Transmission Technique Using Mosaic Image

ISSN(Online): 2320-9801
ISSN (Print): 2320-9798
International Journal of Innovative Research in Computer and Communication Engineering
An ISO 3297: 2007 Certified Organization
Vol.3, Special Issue 3, April 2015
2nd National Conference On Emerging Trends In Electronics And Communication Engineering (NCETECE’15)
Organized by
Dept. of ECE, New Prince Shri Bhavani College Of Engineering & Technology, Chennai-600073, India during 6th & 7th April 2015
Covert Image Transmission Technique Using
Mosaic Image
S.Merlin
II Year ME CSE, Regional Centre of Anna University, Tirunelveli, Tamil Nadu, India
ABSTRACT: Information security is becoming increasingly important in the modern networked age. Secure
Image Transmission has the potential of being adopted for mass communication of sensitive data under the
scrutiny of an adverse censoring authority. Several steganographic techniques for transmitting information
without raising suspicion are found in Literature. However secret-fragment-visible mosaic images allow the
user to securely transmit an image under the cover of another image of same size. This effectively achieves
an embedding capacity of eight bit per pixel. This project presents the secret-fragment-visible mosaic image
technique with nearly reversible color transmission scheme. The secret and target images of same size are
used. The blocks of both images are matched according to the standard deviation of the blocks. To ensure a
positive standard deviation for the secret image, a pseudorandom gaussian noise signal is added to the secret
image. The matching blocks of secret image are transformed according to a reversible color transformation
procedure and arranged to form the mosaic image which is visually similar to the target image. The side
information needed to reverse the color transmission are encoded in binary digits and stored in the mosaic
image using a interpolation error based watermarking procedure. Shared keys are used to shuffle the side
information and to generate the pseudorandom noise. These keys enable the receiver the recover the secret
image with a high precision. The root mean square error and peak signal to noise ratio are used as quality
measures. The experimental results show that the presented method has high embedding rate of with least
distortion.
I.
INTRODUCTION
Images from various sources are frequently utilized and to be transmitted through the internet for various
applications, such as online personal photograph albums, confidential enterprise archives, document storage
systems, medical imaging systems, and military image databases are used. These images usually contain
private or confidential information so that they should be protected from leakages during the secure
transmissions. Recently, many methods have been proposed for securing image transmission, for which two
common approaches are image encryption and data hiding. Image encryption is a technique that makes use
of the natural property of an image, such as high redundancy and strong spatial correlation, to get an
encrypted image. The encrypted image is a noise image so that no one can obtain the secret image from the
unless he/she has the correct key. An alternative to avoid this problem is data hiding that hides a secret
message into a cover image so that no one can realize the existence of the secret data, in which the data type
of the secret message investigated in this paper is an image. which transforms a secret image into a
meaningful mosaic image with the same size and looking like a preselected target image. The transformation
process is controlled by a secret key, and only with the key can a person recover the secret image nearly
losslessly from the mosaic image.
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ISSN(Online): 2320-9801
ISSN (Print): 2320-9798
International Journal of Innovative Research in Computer and Communication Engineering
An ISO 3297: 2007 Certified Organization
Vol.3, Special Issue 3, April 2015
2nd National Conference On Emerging Trends In Electronics And Communication Engineering (NCETECE’15)
Organized by
Dept. of ECE, New Prince Shri Bhavani College Of Engineering & Technology, Chennai-600073, India during 6th & 7th April 2015
II. IMPLEMENTATION
This method includes two main phases as shown by the flow diagram of Fig. 3: 1) mosaic image creation
and 2) secret image recovery. Fig. 1.Flow diagram of the proposed method. In the first phase, a mosaic
image is yielded, which consists of the fragments of an input secret image with color correctionsaccording to
a similarity criterion based on color variations. The phase includes four stages: 1) fitting the tile images of
the secret image into the target blocks of a preselected target image; 2) transforming the color characteristic
of each tile image in the secret image to become that of the corresponding target block in the target image; 3)
rotating each tile image into a direction with the minimum RMSE value with respect to its corresponding
target block; and 4) embedding relevantinformation into the created mosaic image for future recovery of the
secret image. In the second phase, the embedded information is extracted to recover nearly losslessly the
secret image from the generated mosaic image. The phase includestwo stages: 1) extracting the embedded
information for secret image recovery from the mosaic image, 2) Reverse transforming the color
characteristic of each tile image in the secret image to become that of the corresponding target block in the
target image; 3) Reverse rotating each tile image into a direction with respect to its corresponding target
block and 4) recovering the secret image using the extracted information.
The architecture diagram for producing a Mosaic Image then Recover the Secret image from the
Mosaic image is given in Fig 1.
RMSE
The Root Mean Square Error (RMSE) (also called the root mean square deviation, RMSD) is a frequently
used measure of the difference between values predicted by a model and the values actually observed from
the environment that is being modelled. These individual differences are also called residuals, and the
RMSE serves to aggregate them into a single measure of predictive power.
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ISSN(Online): 2320-9801
ISSN (Print): 2320-9798
International Journal of Innovative Research in Computer and Communication Engineering
An ISO 3297: 2007 Certified Organization
Vol.3, Special Issue 3, April 2015
2nd National Conference On Emerging Trends In Electronics And Communication Engineering (NCETECE’15)
Organized by
Dept. of ECE, New Prince Shri Bhavani College Of Engineering & Technology, Chennai-600073, India during 6th & 7th April 2015
The RMSE of a model prediction with respect to the estimated variable Xmodel is defined as the square root of
the mean squared error:
Where Xobs is observed values and Xmodel is modelled values at time/place i.
The calculated RMSE values will have units, and RMSE for phosphorus concentrations can for this reason
not be directly compared to RMSE values for chlorophyll a concentrations etc. However, the RMSE values
can be used to distinguish model performance in a calibration period with that of a validation period as well
as to compare the individual model performance to that of other predictive models.
PSNR
The Peak Signal to Noise Ratio (PSNR) has been used as a benchmark to evaluate new objective perceptual
video quality metrics. There is not currently an international Recommendation specifying exactly how to
perform this critical measurement. Since the calculation of PSNR is highly dependent upon proper
calculation of spatial alignment, temporal alignment, gain, and level offset between the processed video
sequence and the original video sequence, one must also specify the method of performing these calibration
procedures. Since the calculation of PSNR is highly dependent upon proper estimation of spatial alignment,
temporal alignment, gain, and level offset between the processed video sequence and the original video
sequence, the method of measurement for PSNR should ideally include a method for performing these
calibration procedures.
This PSNR calculation method in this Recommendation has the advantage of automatically determining the
highest possible PSNR value for a given video sequence over the range of spatial and temporal shifts. Only
one temporal shift is allowed for all frames in the entire processed video sequence
III. ALGORITHM
Mosaic Image Creation
Stage 1.
Fitting the Tile Images into the Target Blocks.
Step 1. If the size of the target image T is different from that of the secret image S, change the size of T to
be identical to that of S; and divide the secret image S into n tile images {T1, T2, . . . , Tn} as well as the
target image T into n target blocks {B1, B2, . . . , Bn} with each Ti or Bi being of size.
Step 2. Compute the means and the standard deviations of each tile image Ti and each target block Bj for
the three color channels and compute accordingly the average standard deviations for Ti and Bj ,
respectively, for i = 1 through n and j = 1 through n.
Step 3. Sort the tile images in the set Stile = {T1, T2, . . . ,Tn} and
the target blocks in the set S target
= {B1,B2, . . . , Bn} according to the computed average standard deviation values of the blocks. Map
in
order the blocks in the sorted S tile to those in the sorted S target in a 1-to-1 manner; resulting in a mapping
sequence L of the form: T1 → Bj1 , T2 → Bj2 , . . . , Tn → Bjn .
Stage 2.
Performing Color conversions between the Tile Images and the Target Blocks.
Step 4. Create a mosaic image F by fitting the tile images into the corresponding target blocks according to
L.
Step 5. Create a counting table TB with 256 entries, each with an
index corresponding to a residual
value, and assign an initial
value of zero to each entry (note that each residual value will
be in the
range of 0 to 255).
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ISSN(Online): 2320-9801
ISSN (Print): 2320-9798
International Journal of Innovative Research in Computer and Communication Engineering
An ISO 3297: 2007 Certified Organization
Vol.3, Special Issue 3, April 2015
2nd National Conference On Emerging Trends In Electronics And Communication Engineering (NCETECE’15)
Organized by
Dept. of ECE, New Prince Shri Bhavani College Of Engineering & Technology, Chennai-600073, India during 6th & 7th April 2015
Step 6. For each mapping Ti →Bji in sequence L, represent the means μc and μ c of Ti and Bji,
respectively, by eight bits; and represent the standard deviation quotient qc appearing by seven bits, where c
= r, g, or b.
Step 7. For each pixel pi in each tile image Ti of mosaic image F with color value ci where c = r, g, or b,
transform ci into a new value ci; if ci is not smaller than 255 or if it is not larger than 0, then change ci to be
255 or 0, respectively; compute a residual value Ri for pixel pi and increment by 1 the count in the entry in
the counting table TB whose index is identical to Ri
Stage 3.
Rotating the Tile Images.
Step 8. Compute the RMSE values of each color transformed tile image Ti in F with respect to its
corresponding target block Bji after rotating Ti into each of the directions θ =0, 90, 180 and 270 degrees;and
rotate Ti into the optimal direction θ degree with the smallest RMSE value.
Stage 4.
Embedding the Secret Image Recovery Information.
Step 9. Construct a Huffman table HT using the content of the counting table TB to encode all the residual
values computed previously.
Step 10.
For each tile image Ti in mosaic image F, construct a bit stream Mi for recovering Ti,
including the bit-segments which encode the data items.
Secret Image Recovery
Stage 1.
Extracting the Secret Image Recovery Information.
Step 1. Extract from F the bit stream I by a reverse version and decode them to obtain the following data
items,the Huffman table HT for encoding the values of the residuals of the overflows or underflows.
Step 2. Extract the bit stream M t using the values of Ni and N pair by the same scheme used in the last step.
Step 3.
Decrypt the bit stream M t into Mt by K.
Step 4. Decompose Mt into n bit streams M1 through Mn for the n to-be-constructed tile images T1 through
Tn in S, respectively.
Step 5. Decode Mi for each tile image Ti to obtain the following data items: 1) the index ji of the block Bji
in F corresponding to Ti; 2) The optimal rotation angle θ° of Ti; 3) The means of Ti and Bji and the related
standard deviation quotients of all color channels; and 4) The overflow/underflow residual values in Ti
decoded by the Huffman table HT.
Stage 2.Recovering the Secret Image.
Step 6. Recover one by one in a raster-scan order the tile images Ti, i = 1 through n, of the desired secret
image S by the following steps: 1) rotate in there verse direction the block indexed by ji, namely Bji, in F
through the optimal angle θ° and fit the resulting block content into Ti to form an initial tile image Ti; 2) use
the extracted means and related standard deviation quotients to recover the original pixel values in Ti.
Step 7. Compose all the final tile images to form the desired secret image S as output.
IV. EXPERIMENTAL RESULTS
The barchart explains about the PSNR (Peak Signal Noise Ratio) value calculation to evaluate new object
perceptual video quality metrics.
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ISSN(Online): 2320-9801
ISSN (Print): 2320-9798
International Journal of Innovative Research in Computer and Communication Engineering
An ISO 3297: 2007 Certified Organization
Vol.3, Special Issue 3, April 2015
2nd National Conference On Emerging Trends In Electronics And Communication Engineering (NCETECE’15)
Organized by
Dept. of ECE, New Prince Shri Bhavani College Of Engineering & Technology, Chennai-600073, India during 6th & 7th April 2015
Fig 2. PSNR value Calculation Bar Chart
In Fig 2 Blue line indicates without side information of the Mosaic Image,Green line indicates with side
information of Mosaic Image, Brown line indicates the Extraction value of an Secret Image, then X axis
represents the number of experiments, then Y axis represents DataBase.
Fig 3. RMSE value Calculation Bar Chart
The barchart explains about the RMSE (Root Mean Square Error) value of recovered Secret Image to
original Image.
In Fig 3 Blue line indicates without side information of the Mosaic image, Green line indicates with side
information of Mosaic Image, Brown line indicates the Extraction value of an Secret Image, then X axis
represents the number of experiments, then Y axis represents DataBase.
V. CONCLUSION AND FUTURE WORK
A novel method for secret transmission of images is presented. The secret color image is camouflaged into a
target image of the same size to produce a mosaic image. The mosaic image resembles the target image and
is visually indistinguishable from it. The mosaic image creation involves block by block processing of the
images. Gaussian noise is added to the secret image to ensure positive variance of intensities within image
blocks. Image blocks are matched according to the standard deviation of the intensities. Then a color
transformation equation is utilized to transform the secret image blocks into the mosaic image blocks. Side
information required for the accurate reversal of the transformation process is compressed and embedded in
the mosaic image using a least significant bit embedding technique. The performance of the method was
experimentally analyzed using root mean square error and peak signal to noise ratio. It was found that the
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35
ISSN(Online): 2320-9801
ISSN (Print): 2320-9798
International Journal of Innovative Research in Computer and Communication Engineering
An ISO 3297: 2007 Certified Organization
Vol.3, Special Issue 3, April 2015
2nd National Conference On Emerging Trends In Electronics And Communication Engineering (NCETECE’15)
Organized by
Dept. of ECE, New Prince Shri Bhavani College Of Engineering & Technology, Chennai-600073, India during 6th & 7th April 2015
method yields high quality mosaic images and the extraction of the secret image is accurate. In particular the
PSNR of embedding is found to be around 40 dB. The extraction yields a PSNR of above 50 dB. It can be
safely concluded that the method can be used for real time applications that require the secret transmission
of images without raising suspicion in an observing entity. In the future, the visual quality of the mosaic
image could be enhanced further using more parameters of color transformation.
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