Image authentication and tamper detection using fragile...
Transcript of Image authentication and tamper detection using fragile...
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 6, Issue 7, July 2017, ISSN: 2278 – 1323
1058 All Rights Reserved © 2017 IJARCET
Abstract— In this paper a fragile watermarking scheme
based on adaptive embedding rules is proposed. The
watermark embedding and detection rules are applied
according to the characteristics of image blocks. In our
algorithm we have used gray level variance of image blocks to
distinguish it as smooth and unsmooth block. The discrete
embedding rules are applied for smooth and unsmooth blocks
to improve authentication of the image. In order to access the
performance of the proposed scheme copy and paste attack has
been applied to the watermarked images. Experimental results
have been compared with Hsu and Tu’s [9] method and found
significant increase in PSNR of the watermarked images and
decrease in False negative rate and False positive rate.
Index Terms— Fragile watermarking, Image
authentication, Spatial domain, Tamper detection.
I. INTRODUCTION
Due to rapid growth of internet and digital technologies, the
digital images can be easily shared across the globe. At the
same time, the data shared can be easily manipulated with
the help of image processing tools such as Photoshop. The
ease and extent of such manipulations draw the attention to
the need of image authentication. Therefore, verifying the
integrity of digital images has become a crucial research
topic. Image authentication can be implemented using digital
signatures or digital watermarking. A digital signature is an
encrypted or signed hash value of image contents. A digital
signature can detect that an image is modified but its
drawback is that it cannot locate the tampered regions.
Digital watermarking enables not only identifying whether
an image is tampered, but also locate the tampered part.
Thus digital watermarking overcomes the limitation of
digital signatures. Digital watermark based authentication
scheme has two steps: the first is embedding of the
watermark and the second is tamper detection. The digital
watermarking scheme not only identify whether an image is
tampered, but can also locate the tampered part. In digital
watermarking fragile watermarking is used for the purpose of
authentication of the images. Fragile watermarking can be
applied in spatial domain or transform domain. In spatial
domain the intensity values of the pixels are modified
directly to embed the watermark. In case of the transform
Manuscript received July, 2017.
Yadwinder Kaur, Department of Computer Science, Punjabi University
Patiala, India.
Dr. Sukhjeet Kaur Ranade, Associate Professor, Department of Computer
Science, Punjabi University Patiala, India.
domain the transform coefficients of the image are modified
to embed the watermark. We have chosen to work in spatial
domain as it is easy to implement and its computational
complexity is less.
II. LITERATURE SURVEY
The first fragile watermarking technique was proposed by
Walton [1] in 1995 based on the use of the checksum which is
calculated from the 7 most significant bits and embedded in
the least significant bit. It has a limitation that one can
change the image content by keeping the least significant bit
unchanged and it cannot detect the tamper location.
Yeung and Mintzer [2] proposed an algorithm to verify that
an image is not modified by the use of an invisible watermark
embedded into the image pixel values. The watermark
extraction process extracts the embedded watermark from
watermarked image using the verification key. The
limitation of this technique is that watermark can be easily
forged.
Kailasanathan [3] proposed a modified version of the Yeung
Mintzer scheme in order to prevent the two main attacks
proposed for Yeung Mintzer method.
Lee and Lin [4] proposed a scheme that uses the dual
watermark for the purpose of the image tamper detection and
recovery. In proposed scheme every block in the image
contains watermark of further two blocks. For every
non-overlapping block in the image there are two copies of
watermark. Therefore there are two copies of watermark for
the entire image and when first copy is destroyed the second
watermark can be used. A secret key that is send along with
the watermarked image is used to extract the watermark for
tamper recovery purpose.
Zhang and Wang [5] proposed a method based upon novel
fragile watermarking which can recover the original image
from the tampered image. In this scheme a watermark which
consists of reference bits and check bits is embedded into the
original image using data hiding method. In order to find the
tampered image blocks the extracted bits can be compared
with the calculated check bits. The disadvantage of this
technique is that it cannot recover the tampered part if the
tampered area is large.
Rawat and Raman [6] discussed a novel chaos based
watermarking algorithm for image authentication and
.
Image authentication and tamper detection
using fragile watermarking in spatial domain
Yadwinder Kaur, Dr. Sukhjeet Kaur Ranade
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 6, Issue 7, July 2017, ISSN: 2278 – 1323
1059 All Rights Reserved © 2017 IJARCET
tamper detection. The proposed method can detect any
change made to the image and can also show the specific
locations that have been changed. Two chaotic maps are used
to improve the security of the proposed scheme. In this
scheme the initial values of the chaotic maps are used as
secret keys. In order to extract the right watermark the person
should have the correct keys. The scheme has two limitations
first the cat map that is
used to disarrange the host image is appropriate only for
square image. Second it cannot resist collage attack.
Teng et al [7] proposed the scheme that analyzed the security
of a chaotic system based fragile watermarking scheme for
image tamper detection proposed by Rawat and Raman
[6].The proposed method discussed some errors and
modification attacks against Rawat and Raman’s scheme.
The experimental results and theoretical analysis showed
that the fragile watermarking scheme of Rawat is not secure.
This method proposed an improved version in order to
improve the security of the existing technique. Performance
evaluation is carried out by performing the attacks such as cut
and paste attack, text addition and collage attack.
Caragata et al [8] proposed two attacks on Teng et al’s [7]
fragile watermarking scheme. The attacker can apply valid
watermarks on tampered images in both the cases, therefore
making the watermarking scheme useless. The first attack
uses the watermarked version of two chosen images, and the
second attack is a generalization of the first, uses a number of
arbitrary watermarked images. The paper also models the
cryptanalysis process for the second attack using Markov
chains in order to demonstrate that the necessary number of
images is relatively small for a high probability of successful
attack. All the results that are presented in this paper have
been confirmed by a practical implementation.
Hsu and Tu [9] proposed the scheme that uses the
smoothness to differentiate the types of image blocks and use
different watermark embedding, tamper detection, and
recovery schemes for different block types. The performance
of this scheme can be improved further by changing its
embedding rules and tamper detection procedure.
III. PROPOSED METHOD
In this work the embedding and tamper detection procedure
of the existing scheme [9] is modified in order to improve its
efficiency. In the proposed scheme the watermark embedding
rules are defined according to the characteristics of image
blocks. The gray level variance of image blocks is used to
distinguish it as smooth and unsmooth block. The spatial
domain layouts of non smooth and smooth small blocks are
shown in Fig.1 and Fig.2 respectively, where each row
represents eight bits b7, b6, . . ., b0 of a pixel. As is the
authentication information of a small block, Rs is the
recovery information of a small block, Rl is the recovery
information of a large block, Ms is the two bit information
obtained from the two most significant bits of the mean of
each small block.
b7 b 6 b 5 b 4 b 3 b 2 b 1 b 0
A
s
As
A
s
As
A
s
M
s
Rl M
s
Fig. 1 Watermark embedding rules for unsmooth small
blocks
b7 b 6 b 5 b 4 b 3 b 2 b 1 b 0
p0
p1
p2
p3
Fig. 2 Watermark embedding rules for smooth small blocks
The watermarking embedding and tamper detection
procedure is described in detail as follow:
Watermark embedding procedure:
1. Divide the entire image into k = M ×N/64 non-overlapping
large blocks of 8 × 8. Each large block consists of 16 small
blocks of 2×2. M and N are height and width of image
respectively.
2. Compute gray level variance of each large block and sort it
in descending order. The top⌊ (M ×N/)/ (64 × 3) ⌋ blocks are
non-smooth blocks and the remaining are smooth blocks.
3. Iterate for each large block
a) Compute the mean of the large block and convert it in the
binary form and then generate the 16 bit information by
producing combination of first four most significant bits four
times and then most significant bit. Embed these 16 bits in Rl
position of 16 small blocks.
b) Calculate the mean of each 2×2 non-smooth small block
and convert it into binary form. Embed the five most
significant bits of the mean in Rs position.
c) If the current block is the last block then proceed to step 4
else go to step 3 (a).
4. In order to minimize the difference between the
watermarked image and original image the following
equation is used as smoothing function.
(1)
where x’ is the value of the five most significant bits of
original pixel of the image, d is the difference between the
value of three least significant bits after a watermark is
embedded and value of three least significant bits of original
pixel of the image.
5. Input the secret key.
6. Iterate for each small blocks
Rs Rs As
Rs As As
Rs As M
s
Rs Rl M
s
p0
p1
p2
p3
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 6, Issue 7, July 2017, ISSN: 2278 – 1323
1060 All Rights Reserved © 2017 IJARCET
a) Compute Message Digest 5 [10] over the small block and
then apply the XOR over the message digest and secret key.
Fold the information to the 5 bits. If current block is smooth
block the embed the first four bits in the As position else
embed the five bits in As position.
b) Compute the mean of the small block and convert it into
the binary form. Embed the two most significant bits in Ms
position.
c) If current block is the last block then go to step 7 else go to
step 6(a).
7. Compute the performance parameters and return the
watermarked image.
Tamper detection procedure: The image tamper detection
model reverses the whole procedure of embedding and
verifies the embedding to recognize the tampered part. In our
scheme two level tamper detection is performed in order to
detect the tampered regions in the image.
1. Input the tampered image.
2. Divide the entire image into k = M ×N/64 non-overlapping
large blocks of 8 × 8. Each large block consists of 16 small
blocks of 2×2. M and N are height and width of image
respectively.
3. Compute gray level variance of each large block and sort it
in descending order. The top⌊ (M ×N)/ (64 × 3) ⌋ blocks are
non-smooth blocks and the remaining are smooth blocks.
4. Input the secret key used during the embedding process.
5. Initialize a binary image equal to the size of input image,
detectbinary.
6. Iterate for each small block
a) Compute Message Digest 5 [10]over the small block and
then apply the XOR over the message digest and secret key.
Fold the information to the 5 bits. If current block is smooth
block the compare the first four bits with the pre-embedded
information in the As position else compare the 5 bits with the
pre-embedded information in As position.
b) Compute the mean of the small block and convert it into
the binary form. Extract the two most significant bits of the
mean and compare it with the pre-embedded information in
Ms position.
c) If both the information does not match with pre-embedded
information then current 2×2 block is marked as invalid and
the corresponding pixels in detectbinary. are assigned the value
1 (i.e. white color to represent the tampered part).
d) If current block is the last block then go to step 7 else go to
step 6(a).
7. Return the detectbinary image mentioning the tampered
region and compute the performance parameters.
IV. EXPERIMENTATION AND RESULTS
All the experiments are performed in MATLAB 7.10.0
(R2010a) on a PC with 1.70 GHz Intel Core i3 CPU, 4 GB
RAM and 1 TB HDD under windows 8 environment. In
order to evaluate the performance of the proposed system
copy-move attack has been applied on the watermarked
images using the adobe Photoshop. All the images are of size
512×512 and are in tiff format. The performance evaluation
metrics used are Peak Signal to Noise Ratio (PSNR), False
negative rate (FNR), False positive rate (FPR), True positive
rate (TPR) and True negative rate (TNR).
PSNR is measured in decibels and is given by following
equation:
(2)
where MSE is mean square error and is given by:
(3)
where and represents the length and width of the two
dimensional image respectively
In order to measure the tamper detection accuracy FNR, FPR,
TPR and TNR are used.
False negative rate is the proportion of actual tampered pixels
that were erroneously reported as untampered pixels.
(4)
False positive rate is the proportion of actual untampered
pixels that were erroneously reported as tampered pixels.
(5)
True positive rate is the proportion of actual tampered pixels
that are reported as tampered pixels.
(6)
True negative rate is the proportion of actual untampered
pixels that were reported as untampered pixels.
(7)
where represents True Positive , represents False
Negative, represents False Positive and represents
True Negative.
True Positive means the number of pixels that are tampered
and judged as tampered. False Negative means the number of
pixels that are tampered but judged as untampered. False
Positive means the number of pixels that are untampered but
judged as tampered. True negative means the number of
pixels that are untampered and judged as untampered. Table I. PSNR, FPR, FNR, TPR, TNR of proposed scheme.
Image
name
PSNR
(dB) FPR FNR TNR TPR
Baboon 43.70 0.0035 0.0002 0.9965 0.9998
Barbara 43.84 0.0006 0.0002 0.9994 0.9998
Jet plane 44.47 0.0017 0.0004 0.9983 0.9996
Pirate 43.84 0.0011 0.0004 0.9989 0.9996
Pepper 43.82 0.0041 0.0003 0.9959 0.9997
Lena 43.92 0.0031 0.0014 0.9969 0.9986
Gold 43.77 0.0030 0.0007 0.9970 0.9993
Bridge 44.29 0.0006 0.0004 0.9994 0.9996
Couple 43.90 0.0027 0.0007 0.9973 0.9993
Zelda 43.78 0.0025 0.0007 0.9975 0.9993
Elaine 43.76 0.0018 0.0004 0.9982 0.9996
Sailboat 43.72 0.0020 0.0002 0.9980 0.9998
Average 43.90 0.0022 0.0005 0.9978 0.9995
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 6, Issue 7, July 2017, ISSN: 2278 – 1323
1061 All Rights Reserved © 2017 IJARCET
Table II. Comparison between experimental data of proposed
method and Hsu and Tu’s scheme [9].
(a) (b) (c) (d)
(e) (f) (g) (h)
( i) (j) (k) (l)
Fig. 3 Watermarked images (a) Baboon (b) Barb (c) Jet plane (d) Pirate (e) Pepper (f) Lena (g) Gold
(h) Bridge (i) Couple (j) Zelda (k) Elaine (l) Sailboat
(a) (b) ( c) (d)
(e) (f) (g) (h)
Method
PSNR
(dB) FPR FNR TNR TPR
Proposed 43.90 0.0022 0.0005 0.9978 0.9995
Hsu and
Tu’s 40.72 0.0047 0.0008 0.9953 0.9992
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 6, Issue 7, July 2017, ISSN: 2278 – 1323
1062 All Rights Reserved © 2017 IJARCET
(i) (j) (k) (l)
Fig. 4 Copy-move attack experiment (a) Baboon (b) Barb (c) Jet plane (d) Pirate (e) Pepper (f) Lena
(g) Gold (h) Bridge (i) Couple (j) Zelda (k) Elaine (l) Sailboat
(a) (b) (c) (d)
(e) (f) (g) (h)
(i ) (j) (k) (l)
Fig.5 Tamper detection results of copy move attacks (White zones are tampered zones).
V. CONCLUSION
In this paper fragile watermarking scheme based on
adaptive embedding is implemented. The experimental
results of PSNR show that the watermarked images have
high visual imperceptibility. Comparison results of Table
II show that our scheme outperforms the existing
technique by reducing false positive rate and false negative
rate and increasing the PSNR of watermarked images.
Future scope of this work includes extending this scheme
for authentication of color images.
ACKNOWLEGDEMENT
With profound gratitude and due regards , I whole
heartedly and sincerely acknowledge the efforts,
encouragement and proper guidance by Dr. Sukhjeet Kaur
Ranade Associate Professor, Department of Computer
Science, Punjabi University, Patiala .
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[6] S. Rawat and B. Raman, "A chaotic system based fragile
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International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume xx, Issue xx, Month 20xx, ISSN: 2278 – 1323
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[7] L. Teng, X. Wang and X. Wang, "Cryptanalysis and
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Electronics and Communications 67.6 (2013): 540-547.
[8] D. Caragata, et al, "Cryptanalysis of an improved fragile
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[9] C.S. Hsu and S.F. Tu,"Image tamper detection and recovery
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[10] R. Rivest, RFC1321: The MD5 message-digest algorithm,
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Yadwinder Kaur is a student of M.Tech (CSE) in
Department of Computer Science, Punjabi University, Patiala
carrying out her research work under the guidance of Dr.
Sukhjeet Kaur Ranade. Her main research interest is digital
watermarking.
Dr. Sukhjeet Kaur Ranade is presently serving
as Associate Professor in Department of Computer Science,
Punjabi University, Patiala. Her key research areas are image
processing and information hiding. She has published more than
50 papers in various journals and conferences of international
and national repute.