International Journal of Engineering Trends and Technology (IJETT) – Volume 24 Number 1- June 2015
Tampering Detection Based On Blur Inconsistencies
Vidyalaxmi Garur#1, Dr.Lalitha Y S *2
#
P G student, Digital Electronics, * P G Head, Dept. of E & CE, Appa institute of engineering and technology,
Gulbarga India
Abstract— Image tampering is a digital art which needs
understanding of image properties and good visual
creativity. The tampering of image is done for many reasons
either to enjoy fun of digital works creating pleasant photos
or to produce false evidences. In this paper, we propose a
method for image tampering detection based on blur
inconsistency detection and classification. The image is
partitioned into blocks, after the block based partitioning,
for local blur kernels estimation a space variant prior for
local blur kernel is proposed. To cluster the image blocks
which have similar blur kernels into different regions kmeans clustering is used and the clusters are classified as
the different blur types. The experimental result shows there
is an inconsistency in the blur types of an images by
considering the difference value between the motion blur
,out of focus blur and the original image which is the proof
for the image tampering.
Keywords— Image splicing, Motion blur, Out of focus blur,
Partial blur detection, Tampering detection.
I. INTRODUCTION
Adding or removing the important features from an
image is called as image tampering. In terms of image
processing, changing the original image information
by modifying the pixel values to a new values is
known as image tampering. This means enhancing the
image quality in order to clearly express the content of
the image. Tampering digital images from their time
of capture with an intention to change its original
information is called digital image tampering. It is also
called as image forgery. In order to produce false
evidences tampering is done to the image by covering
the objects in the original image. Image tampering can
be threat to security of people and society.
There are many types of image tampering
techniques, one of the most common type of
tampering is image splicing. In this method, if there is
a blur type difference in original image and spliced
region such as out of focus blur and motion blur, then
there is an inconsistency in blur may be present in the
tampered image. The aim of the present work is image
splicing detection by exploring the inconsistency in
the partial blur types.
The image forensic techniques are divided into
active and passive. There are different categories in
the passive technique such as (1) Format-based[1]; (2)
Camera-based such as demos icing regularity [2-4],
and sensor pattern noise[5]; (3) Pixel-based such as resampling[6] and contrast enhancement detection[7]; (4)
Physically-based such as light anomalies[8]. There are
some limitations with each techniques.
ISSN: 2231-5381
Based on the blur degree inconsistency some works
have been proposed for tampering detection[9-13]. For
splicing detection Kakar et al. [14] proposed a method
based on inconsistency in the motion blur degree and
direction. But this method is only applicable for
motion blur.Based on alpha channel Su et al.[15]
proposed a technique for segmentation of motion and
out-of-focus blurred regions. The limitation of the
methods in [15]. Based on magnitude of cepstrum
coefficients Aizenberg et al [16] proposed a technique
for classification of motion, Gaussian and uniform
blurred blocks.The methods in[15-16] have the
limitation that they do not high performance for partial
blur type detection.The image is portioned into the
blocks and a feature is used to classify the blur type of
the blocks. In real situation the size of the block
effects the blur types of the image blocks. A two step
approach is proposed for detection of blur types at
block levels.
.
II. TYPES OF IMAGE TAMPERING
The commonly used techniques for
tampering are as follows.
image
A. Copy-move
This is the commonly used type of image tampering
technique, which includes the adding or removing of
information to cover any part of the original image.
The texture areas have the same properties to that of
the image, so it is unperceivable for human eye
investigation.
B. Image Slicing
Image splicing is obtained by sticking different
photographic images together. The art of creating
composite photograph which can be tracked back to
the time of camera invention
is called as
photomontage.
C. Resize
This can be used for performing geometric
transformation of an image such as shrinking or
enlarging the size of an image or part of an image
D. Cropping
Cropping is a technique to cut-off borders of an
image or reduces the canvas on which an image is
displayed. Generally this kind of operation is used to
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International Journal of Engineering Trends and Technology (IJETT) – Volume 24 Number 1- June 2015
remove border information which is not important for
display
Most of the values in the motion blur kernels are
zero, so motion blur kernels tend to be sparse while
the tendency of sparse in out of focus blur kernels is
III. LOCAL BLUR KERNEL ESTIMATION
less. Sparse in low blur degree kernels are more than
Given a color image A of size M×N, we first the high blur degree, this means the kernel sparseness
convert this image into gray scale image G and then G depends on the blur degree. The distribution of motion
is partitioned into non overlapping blocks Gi,j with blur kernels are closer to hyper-laplacian while the
P×P pixels, where I and j are index of blocks (1≤ i ≤ one for out of focus blur kernels are closer to
Gaussian. By choosing prior model closer to hyper, 1≤ j ≤ ). The image blurring process for an image laplacian for motion blur kernels and a prior model
closer to Gaussian for out of focus blur kernels better
block Gi,j is represented by
results can be obtained.
Gi,j = Ii,j ∗ Ki,j + Ni,j
IV. SIMILARITY BASED CLUSTERING OF LOCAL BLUR
(1)
KERNELS
Where Ii,j is a sharp image block, Ki,j is a local blur
kernel, Ni,j is the image block noise and * denotes
convolution.
Blind Image Deconvolution is used to estimate Ki,j
from Gi,j, which estimate Ki,j and Ii,j from Gi,j . The
method in [20] is udes to solve Blind Image
Deconvolution. For better results we need to choose
accurate models for Ki,j and Ii,j
The statistics of motion blur and out of focus
kernels is studied to find appropriate prior for local
blur kernels. We blur the 400 sharp images with
motion blur and out of focus with different
specifications and the local blur kernels of the images
are estimated. Fig. 1(a) and 1(b) plots the pixel
distribution of motion blur and out of focus blur
kernels respectively.
Fig. 1(a). Pixel value distribution of motion blur
kernels.
Fig. 1(b). Pixel value distribution of out of focus
blur kernels
ISSN: 2231-5381
For blur kernel estimation , to increase the accuracy
of blur kernel it is advantageous to use the large
region of the blurred image. To achieve better results
in the blur kernel estimation the blur in the region
should be invariant in terms of type. In this step, to
generate a space-invariant blur type regions the image
blocks with similar blur kernels are clustered together.
Here we use k-means clustering by taking the intensity
of local blur kernels pixels and the coordinates of the
image blocks in the image as the input features.
Given a set of blur kernels K1,1, K1,2, ….K , of an
image where M×N and P×P are the size of image and
image blocks, respectively. The clustering feature
vector is defined as a d-dimensional vector including
coordinates (i , j)and the pixel intensity of the local
blur kernels of the image blocks. For local blur kernels
of size K×K and (i , j) as horizontal and vertical
coordinates in the image, the feature vector is defined
as V=[ Ki,j (1,1), Ki,j (1,2),… Ki,j (K, K),I,j] with
d=K×K+2 as the input. The k-means clustering
partitions the image blocks into s R1,R2,…Rs to
minimize the sum of squares between pixels of kernels
within the cluster,where sis the number of clusters.
V. REGION BLUR TYPE CLASSIFICATION FOR THE
DETECTION OF IMAGE TAMPERING.
The image G is segmented into s regions, then the
image G is represented by s layers formation model as
G = η1×R1+... + ηs × Rs, where R1, ...,Rs are the
regions and η1, ..., ηs
are the binary masks representing the regions. Now
the image blrring procees in eq.1 is formulated as G =
η1 ×(I1 ∗K1 +N1)+... + ηs × (Is ∗ Ks + Ns), where
K1, ...,Ks are the blur kernels of s regions R1, ...,Rs.
Minimum distance classifier is used to identify the
blur type of the regions which measures the
normalized cross correlation of the estimated blur
kernels K1, …...., Ks and a set of candidate motion
blur kernels {Km1 ,...,Kmu } and out-of-focus blur
kernels{Ko1 , ...,Kov} with different specifications.
Finally the evaluation is needed to detect any
inconsistency between the blur type and the image
region. If one region has a motion blur and the other
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International Journal of Engineering Trends and Technology (IJETT) – Volume 24 Number 1- June 2015
has a out of focus blur then we can say this as
Fig.4 Edge detection of Image
inconsistency in the blur types.
VI. RESULTS
Given a color image A this is converted into gray
scale and the image is partitioned into number of
blocks and the local blur kernels of the image block is
estimated. K-means clustering is used to cluster the
image blocks based on similarity. These clusters are
classified into different blur types. The accuracy of the
tampering detection is affected by the number of
clusters. The results are shown as follows
Fig.5. Blur reconstruction
Fig.2. Original Image
Fig. 6. Deblurred image
Fig.3.Gray scale Image
Fig.7. Out put Image of Tampering Detection
VII.
CONCLUSION
A novel method for image tampering detection is
proposed
based on partial blur detection and
classification. The input image is partitioned into
number of non overlapping blocks and are used for
local blur kernel estimation. To categorize the image
blocks with similar blur kernels into different regions
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International Journal of Engineering Trends and Technology (IJETT) – Volume 24 Number 1- June 2015
clustering method is used. Then the blur kernels of the
clusters are estimated and the clusters are classified
into different blur types .The experimental results
shows that this proposed method can be used for
tampering detection.
ACKNOWLEDGMENT
Authors would like to thank Department of
Eectronics and Communication, Appa institute of
engineering and technology, Gulbarga, whose timely
support and suggestions went along in the completion
of the project.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
T. Bianchi and A. Piva, “Image Forgery Localization via
Block-Grained Analysis of JPEG Artifacts,” IEEE Trans. Inf.
Forensics Security, vol. 7, no. 3, pp. 1003-1017, 2012.
H. Cao and A. C. Kot, “Accurate Detection of Demosaicing
Regularity for Digital Image Forensics,” IEEE Trans. Inf.
Forensics Security, vol. 4, no. 4, pp. 899-910, 2009.
P. Ferrara, T Bianchi, A. D. Rosa and A. Piva, “Image
Forgery Localization via Fine-Grained Analysis of CFA
Artifacts,” IEEE Trans. Inf. Forensics Security, vol. 7, no.5,
pp. 1566-1577, 2012.
A. Swaminathan, M. Wu, and K. J. R. Liu, “Digital Image
Frensics via Intrinsic Fingerprints,” IEEE Trans. Inf.
Forensics Security, vol. 3, no. 1, pp. 101-117, 2008.
M. Chen, J. Fridrich, M. Goljan, and J. Lucas, “Determining
Image Origin and Integrity Using Sensor Noise,” IEEE Trans.
Inf. Forensics Security, vol. 3, no. 1, pp. 74- 89,2008.
C. Popescu and H. Farid, “Exposing Digital Forgeries by
Detecting Traces of Resampling,” IEEE Trans. Signal
Processing, vol. 53, no. 2, pp. 758-767, 2005.
M. C. Stamm, and K. J. R. Liu, “Forensic Detection of Image
Manipulation Using Statistical Intrinsic Fingerprints,” IEEE
Trans. Inf. Forensics Security, vol. 5, no.3, pp. 492-506, 2010.
M. K. Johnson and H. Farid, “Exposing Digital Forgeries in
Complex Lighting Environments,” IEEE Trans. Inf.
Forensics Security, vol. 2, no.3, pp. 450-461, 2007.
K. Bahrami, A. C. Kot, and J. Fan, “Splicing Detection in
Out-of-Focus Blurred Images,” in Proc. WIFS, pp. 144-149,
2013.
Y. Sutcu, B. Coskun, H. T. Sencar, and N. Memon,“Tamper
Detection Based on Regularity of Wavelet Transform
Coefficients,” in Proc. ICIP, pp. 397-400,2007.
J.Wang, G. Liu, B. Xu, H. Li, Y. Dai, and Z.Wang, “Image
Forgery Forensics Based on Manual Blurred Edge
Detection,” in Proc. Multimedia Information Networking and
Security, pp. 907-911, 2010.
G. Cao, Y. Zhao, and R. Ni, “Edge-based Blur Metric for
Tamper Detection,” Journal of Information Hiding and
Multimedia Signal Processing, vol. 1, no. 1, pp. 20-27, 2010.
X. Wang, B. Xuan, and S. Peng, “Digital image forgery
detection based on the consistency of defocus blur,” in Proc.
Intelligent Information Hiding and Multimedia Signal
Processing, pp. 192-195, 2008.
P. Kakar, N. Sudha, and W. Ser, “Exposing Digital Image
Forgeries by Detecting Discrepancies in Motion ,” IEEE
Trans. on Multimedia, vol. 13, no. 3, pp. 443- 452, 2011.
B. Su, S. Lu, and C. L. Tan, “Blurred Image Region
Detection and Classification,” in ACM Multimedia, pp. 13971400, 2011.
I. Aizenberg, D. V. Paliy, J. M. Zurada, and J. T. Astola,
“Blur Identification by Multilayer Neural Network Based on
Multivalued Neurons,” IEEE Trans. on Neural Network, vol.
19, no. 5, pp. 883-898, 2008.
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