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Journal of Information Security and Applications 45 (2019) 44–51
Contents lists available at ScienceDirect
Journal of Information Security and Applications
journal homepage: www.elsevier.com/locate/jisa
Geometric transformation invariant block based copy-move forgery
detection using fast and efficient hybrid local features
Badal Soni a , 1 , ∗, Pradip K. Das b , Dalton Meitei Thounaojam
a
a Computer Science and Engineering Department, National Institute of Technology Silchar, India b Computer Science and Engineering Department, Indian Institute of Technology Guwahati, India
a r t i c l e i n f o
Article history:
Available online 15 January 2019
Keywords:
MSER
SURF
SIFT
Copy-move
a b s t r a c t
Copy-move forgery in images is a popular tampering method, in which a portion of an image is copied
and pasted on some other location of the same image. This paper proposes an enhancement of block
based copy-move forgery detection using hybrid local features extraction. In this system, the image is
divided into non-overlapping blocks and SURF features are computed from each block. SURF features
are matched using 2NN procedure and large blocks are formed by considering the eight neighboring
blocks of each SURF features match block. Maximally Stable Extremal Regions are detected from each
large region and the extracted SURF descriptors from these regions are compared for matching. Finally,
affine transform is applied to remove the outliers. The proposed system is experimented using MICC-
F220, MICC-F20 0 0 and MICC-F60 0 benchmark datasets and it yields better performance in comparison
with state of the art techniques.
© 2019 Elsevier Ltd. All rights reserved.
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1. Introduction
Due to increase in the availability of low cost and open source
image handling software such as Photoshop, Paint Shop, Photo-
scape, PhotoPlus, GIMP and Pixelmator, the manipulation of dig-
ital images have become easier and a common practice. Using
these powerful tools, it has become absolutely unfeasible to visu-
ally identify whether a given image is original or a manipulated
version. In copy-move forgery, a portion of the image is copied
and pasted into one or more regions of the same image. Since the
copied region comes from the same image, the color palette, noise
components, dynamic range and the other properties will be com-
patible with the rest of the image. Therefore, it is a very challeng-
ing task to detect copy-move forgery in digital images. Generally
to make the forgery unnoticeable, some operations like scaling, fil-
tering, noise embedding, etc. are performed either onto the whole
forged image or copied region of the image before pasting it on
the image.
There are two broad categories of copy-move forgery detection
techniques: block and key-points based. Block-based techniques
usually start by dividing the forged image into either overlapping
or non-overlapping blocks. Thereafter, extraction and matching of
∗ Corresponding author.
E-mail address: [email protected] (B. Soni). 1 http://cse.nits.ac.in/badal/.
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https://doi.org/10.1016/j.jisa.2019.01.007
2214-2126/© 2019 Elsevier Ltd. All rights reserved.
lock features are performed. Scale Invariant Feature Transform
SIFT) [1] and Speeded Up Robust Feature (SURF) [2] are mostly
sed in key-points based copy-move forgery detection techniques.
o increase the reliability, features are represented with a set of
escriptors. Each descriptor is matched with others to find the
orged regions in image. A comprehensive overview of CMFD tech-
iques can be found in [3] . This paper highlighted the pros and
ons of the existing CMFD approaches and future directions in this
omain. Recently a detailed review on block-based and key-points
ased CMFD techniques with the critical discussion of each tech-
iques is given in [4] .
It is observed that block based methods have high computa-
ional cost due to matching of numerous overlapping blocks and
ess efficient against geometric transformation attacks since block
eatures are sensitive to scale and rotation transformation. How-
ver, key-point based methods are popular in copy-move forgery
etection, since they are invariant to rotation and scaling.
To reduce the computational cost and increase the detection
ccuracy against geometric transformation attacks, this paper uti-
izes SURF and MSER because SURF is faster due to lower dimen-
ion features descriptor and invariant to rotation and scaling and
SER is invariant to affine transformation. In this proposed tech-
ique, non-overlapping blocks division is performed to reduce the
umber of blocks. As a consequence, less number of block match-
ng and lower dimension SURF descriptors are extracted from each
lock rather than high dimension SIFT descriptors.
B. Soni, P.K. Das and D.M. Thounaojam / Journal of Information Security and Applications 45 (2019) 44–51 45
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The proposed system is divided into two parts. First, the input
orged image is divided into non-overlapping blocks. SURF points
re detected from each block, matching is performed between the
xtracted descriptors of blocks to get the similar blocks. Secondly,
he paired matched blocks are used to form large regions by con-
idering the eight neighboring blocks of each paired blocks. MSER
egions are detected from each large region and SURF features are
xtracted and matched using the 2NN procedure. Affine transform
s applied to remove the outliers, if any. Finally, the forged regions
re located in the image using the locations of matched regions.
he proposed system is tested using MICC-F220, MICC-F20 0 0 and
ICC-F600 databases.
The main contributions of this paper are:
i. Propose an efficient approach for copy-move forgery detection
based on SURF and MSER.
ii. An enhancement in block based copy-move forgery detection
is proposed by considering eight neighboring blocks of match
block pair to form large blocks, thereby increasing the proba-
bility of detecting the forged region completely.
ii. The formation of large blocks lead in reduction of the number
of matched blocks, thereby reducing the matching time and ul-
timately decreasing the overall computation time of the algo-
rithm.
The paper is organized as follows: Section 2 address existing
ork in copy-move forgery detection. Section 3 describe the back-
round knowledge. Section 4 describe the proposed methodology.
xperimental results and discussions are detailed in Section 5 . Fi-
ally, conclusions and future scope are drawn in Section 6 .
. Related work
In this section some of the related key-points and block based
echniques are covered. In [5] , SIFT key-points are extracted from
mage and they are matched using Euclidean distance. In paper [6] ,
luster of key-points are matched rather than a single key-point.
bject shape, along with texture analysis are used to compare the
ontent of the two matching objects. It is observed that false pos-
tive rate of this paper is less than the paper [5] . A generalized
NN procedure for SIFT descriptors matching is proposed in [7] for
MFD. It is reported that some improvements are needed in the
etection phase for copied image patch with highly uniform tex-
ure where salient key-points are not recovered by SIFT.
In [8] , SURF descriptors are extracted by constructing a square
egion around the considered SURF key-points. Matching is per-
ormed between subsets of key-points descriptors. It is observed
hat this method is fast as well as reliable for small size images.
owever, localization of forgery cannot be done. Hierarchical Ag-
lomerative Clustering (HAC) is introduced for forgery detection in
9] . Initially, SURF descriptors are extracted from the image and
AC is used for cluster matching of the SURF descriptors. It is ob-
erved that this method is fast due to HAC matching. However, de-
ection accuracy of this method is quite low. In [10] , AKAZE fea-
ures and nonlinear scale space are used for object removal with
niform background and replication type of forgeries.
Fusion of block and SIFT key-point methods is given in [11] . In
his paper, SIFT key points are extracted from image regions; after
hat classification of regions into smooth or non-smooth regions
ased upon the ratio of number of key-points and total number
f pixels in that region is carried out. Thereafter, Zernike moments
re used for smooth regions and SIFT is used to non-smooth re-
ions to detect forgery. Finally, outliers are removed by post pro-
essing. It is observed that computational cost of this method in-
reases due to the fusion of Zernike moments and SIFT.
Block division concept has been used by many researchers for
mage manipulation detection. Some of the related papers are cov-
red in this section. In [12] , a block-based copy-move forgery de-
ection method using the quantized DCT coefficients is given. This
ethod is robust to noise, compression and retouching. However,
t is unable to detect forged regions in case of copied blocks being
otated or scaled. In [13] , the original image is divided into a se-
ies of overlapping blocks for block-wise tampering detection and
ocalization. In [14] , two dimensional DWT is first applied on the
orged image and only approximate DWT coefficients are consid-
red for block division. Matching is performed by calculating the
istance between all block pairs. It is observed that this algorithm
s not suitable for forgery detection in case of geometric transfor-
ation attacks.
In [15] , Histogram of Orientated Gradients (HOG) based statisti-
al features are used for forgery detection. It can be observed from
he results that the algorithm needs improvement in detection of
orgery in case of rotation and scaling performed over large re-
ions. In [16] , a perceptual image hashing scheme based on block
runcation coding is given. This paper, utilizes center-symmetrical
ocal binary pattern as image feature descriptor. It has low compu-
ational cost and is invariant to gray scale changes. In [17] , a Multi-
evel Dense Descriptor (MLDD) extraction method and a hierarchi-
al feature matching technique is proposed. The MLDD extract the
ense feature descriptors from each pixel and hierarchical feature
atching is used to detect forged regions.
In [18] , an improved block based technique is presented by di-
iding the image into circular blocks and extracting local and in-
er image features using Discrete Radial Harmonic Fourier Mo-
ents (DRHFMs). It is observed that the computational cost of
his method is higher in comparison to state of the art key-
oints based methods due to overlapping circular blocks division
nd DRHFMs. Recently, Local Binary Patterns Histogram Fourier
eatures and Fast Walsh Hadamard transform based block-based
MFD systems have been proposed in [19] and [20] respectively.
In this paper, we combined the non-overlapping blocks concept
long with MSER and SURF local features for reducing the compu-
ational cost and increasing the detection accuracy in the presence
f geometric transformation attacks.
. Background knowledge
.1. Speeded up robust features (SURF)
Speed Up Robust Features (SURF) is a scale and rotation invari-
nt interest point descriptor. SURF has faster matching speed than
IFT due to Hessian matrix approximation and integral image. Let
n image I and a point ( x, y ) be given then the integral image
integral is calculated as:
integral (x, y ) =
i � x ∑
i =0
j� y ∑
j=0
I(x, y ) (1)
SURF key-points are detected by Fast-Hessian detector that is
ased on the approximation of the Hessian matrix for a given input
mage point. Haar wavelets are used for orientation assignment,
efore the key-point descriptor is formed from the wavelet re-
ponses in the neighborhood of the key-point. Calculation of SURF
escriptor is based on the following steps:
1. Fast Interest Point Detection : The SURF interest point detec-
tion is based on the Hessian matrix. Given a point p = (x, y ) in
an image I , the Hessian matrix H ( p, σ ) in p at scale σ is defined
as follows: [C ( p, σ ) xx C ( p, σ ) xy
C ( p, σ ) xy C ( p, σ ) yy
]
Where C ( p, σ ) xx is the convolution of the Gaussian second order
derivative �2
2 G (σ ) with the image I at point p . The approxima-
�x46 B. Soni, P.K. Das and D.M. Thounaojam / Journal of Information Security and Applications 45 (2019) 44–51
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tion of the second order derivatives is denoted as C xx , C yy and
C xy . By choosing the weights for the box filters adequately, an
approximation for the Hessians determinant is found.
det(H approx ) = C xx C yy − 0 . 9(C xy ) 2 (2)
Finally, the maxima of the determinant of the approximated
Hessian matrix are interpolated in scale and image space.
2. Interest Point Descriptor : In order to assign a unique orien-
tation, SURF creates a circular division around the detected in-
terest points. The orientation is calculated by Haar wavelet re-
sponses in both x and y direction. Finally, the wavelet responses
in horizontal d x and vertical d y are added over each sub-block.
Absolute values | d x | and | d y | are added in order to obtain infor-
mation about the polarity of the image intensity changes. The
resulting descriptor vectors for all 4 × 4 sub-regions of length
64 are generated.
3.2. Maximally stable extremal regions (MSER)
Maximally Stable Extremal Region (MSER) was proposed in [21] .
MSER is a widely used approach for finding the correspondence
between the two images. Such maximally stable regions are de-
termined by varying a threshold, T and then observing the corre-
sponding variation of the size of the local regions in the thresh-
olded image. T is varied as
0 < T < I (3)
where,
I = max (P ) (4)
and P is the pixel values in the image. A region is considered to
be maximally stable if the variation of T brings a change C , ( C < S )
where S is the maximum allowed variation.
4. Proposed methodology
In this paper, we propose an enhanced geometric transforma-
tion invariant block based copy-move forgery detection method us-
ing hybrid local features technique. Initially the forged image is di-
vided into 8 × 8 non-overlapping blocks. SURF points are detected
from each block then scale and rotation invariant SURF descriptor
of length 64 is extracted from each detected SURF point. After that,
the matched blocks are identified. To improved the efficiency of
the proposed forgery detection system, the eight connected neigh-
boring blocks of match block are considered to form a large sized
block by combining all the connected eight neighboring blocks. In
the next step, MSER regions are detected from each large block.
SURF features are then extracted from the detected MSER regions.
Affine transform is applied to the extracted features to remove out-
liers. Extracted features are matched to get the final copy-move
portion(s) in the image. The flow diagram of the proposed method
is given in Fig. 1 . The complete proposed algorithm is explained in
the following steps:
4.1. Preprocessing
In this step, if the input forged image is an RGB image then
convert it into gray scale image I using the following Equation:
I = 0 . 2989 R + 0 . 587 G + 0 . 114 B (5)
Image I is partitioned into n × n size non-overlapping blocks. In
this system we consider n to be 8.
.2. SURF Features detection and descriptor extraction
In this step SURF interest points are detected from each non-
verlapping block. Detection of SURF interest points is based
pon the integral image and Hessian matrix. SURF descriptors are
ormed from the Haar wavelet responses in the neighborhood of
he key-points. The wavelet responses in horizontal d x and verti-
al d y are added over each sub-block. Absolute values | d x | and | d y |
re added in order to obtain information about the polarity of the
oint intensity changes. Hence, the underlying intensity pattern of
ach point is given by a descriptor vector V of dimension 64.
=
[ ∑
(d x ) , ∑
(d y ) , ∑
(| d x | ) , ∑
(| d y | ]
(6)
.3. Descriptor matching
Descriptors matching is performed by comparing each SURF
escriptor with other remaining descriptors. SURF descriptor is a
4 dimensional feature vector and hence exhaustive matching ap-
roach is not a good option. Instead of exhaustive matching, in
his paper, we used two nearest neighbors (2NN) procedure. In
NN procedure, the distance ratio between the closest neighbor
nd second-closest neighbor is compared with a threshold. For a
iven key-point, let the distance vector ED be denoted by ED = d 1 , d 2 , . . . . . . .d n } represents the sorted Euclidean distances of each
escriptor with other descriptors. The 2NN procedure is given as
ollows:
d i d i +1
� δ where δ ∈ (0 , 1) and i ∈ n (7)
he key-point is considered to be matched only if Eq. (7) is
atisfied. In this work, the experimental threshold value δ = 0 . 6
ives the optimum result. Corresponding location of matched key-
oints are stored. Based on the location of matched key-points the
atched blocks are identified.
.4. Eight-connected neighborhood region
To improve the detection results in this step, eight-connected
eighborhood concept is used to find the eight neighboring blocks
f each matched blocks pair. Combination of all neighboring blocks
ive a larger region of the image. The reason behind the formation
f large block is to cover the complete probable forged region in
he image. Consider the scenario that, if size of the copied por-
ion is more than the block size, in this case copied portion lies
n two or more blocks. Large block consider all the eight neighbor-
ng blocks. Therefore, the probability of covering complete copied
ortion of the image in a large region is high. This arrangement
ontribute to the increase of true positive rate of the proposed ap-
roach. These regions will be used in next step for further process-
ng.
.5. MSER Region detection and matching
MSER regions are connected regions characterized by sweeping
he intensity threshold. For finding the MSER regions first extremal
egions, are extracted from the large suspected regions. An appro-
riate threshold is selected using Eqs. (3) and (4) for which an ex-
remal region is Maximally Stable. MSER regions are detected from
he large blocks formed in the previous step. After that, from each
SER region, SURF features are extracted and matched using 2NN.
his matching gives the final probable matched regions in the tam-
ered image, if any. MSER is invariant to affine transformation and
URF is fast local descriptor and is invariant to scale and rotation.
herefore, this hybridization of local features is expected to make
he proposed algorithm more robust against geometric transforma-
ion attacks.
B. Soni, P.K. Das and D.M. Thounaojam / Journal of Information Security and Applications 45 (2019) 44–51 47
Fig. 1. Flow diagram of the proposed algorithm.
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Table 1
Databases description.
Type/Version Image size No. of images
MICC-F220 722 × 480 to 800 × 600 220
MICC-F20 0 0 2048 × 1536 20 0 0
MICC-F600 800 × 533 to 3888 × 2592 600
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.6. Outliers points removal and decision of forgery
The previous step matched MSER regions and these may have
ome outliers. To remove or reduce these outliers, affine transfor-
ation is estimated between the matched MSER features. This pro-
ess removes the outliers present in the features matching and
tore the remaining valid feature’s matched points in an array
amed as Match . Eq. (8) is used to declare if an image is forged or
ot which will be used for performance evaluation. If it is forged,
hen using the location of matched points, forgery is localized in
he tampered image.
f or ged =
{T r ue if Match � 1
F alse other wise, (8)
As MSER is invariant to affine transformation and SURF de-
criptor is invariant to scaling and rotation, the proposed approach
s invariant to geometric transformation attacks. In addition to
his, the proposed technique consider non-overlapping blocks di-
ision rather than overlapping blocks division. Hence, the number
f blocks will be less and as a consequence, blocks matching will
equire less processing time. Therefore, the overall computational
ost of the proposed method is going to be reduced.
. Experimental results and discussions
In this section, description of different benchmarking databases
sed for experimental purpose, performance evaluation measures,
obustness test of the proposed algorithm, analysis of the experi-
ental results and comparison of the proposed approach with ex-
sting approaches are given.
Database : We evaluate the proposed algorithm using different
ublicly available datasets. In this work, we report the experi-
ental results on MICC-F220, MICC-F20 0 0 and MICC-F60 0 image
atasets [7] . MICC-F220 dataset consists of a total of 220 images,
f which 110 are tampered and 110 original images. MICC-F20 0 0
ataset consists of a total of 20 0 0 images of which 70 0 are tam-
ered and 1300 are original images. Forged images are generated
n the same way as MICC-F220. A new dataset MICC-F600 is in-
roduced in [22] containing realistic and challenging tampering at-
acks. In this dataset, 440 are original and 160 are tampered im-
ges containing realistic and challenging copy-move attacks. De-
cription of publicly available databases is given in Table 1 .
Performance Evaluation : Experiments are performed in an HP
achine, Intel Core i5-3230M (2.60 GHz), 4 GB memory. Perfor-
ance of the proposed forgery detection algorithm is evaluated
y calculating the false positive rate (FPR) and True positive rate
TPR), defined as follows:
P R =
Number of original images det ect ed as f orged
T otal number of original images (9)
P R =
Number of images det ect ed as f orged being f orged
T otal number of f orged images (10)
Robustness Test : To check the robustness and susceptibility of
ur method, experiments are performed using the above men-
ioned datasets for the following cases:
i. Copy regions and moved without any geometric transformation.
ii. Copy regions and moved after rotation.
ii. Copy regions and moved after scaling.
iv. Copy regions and moved after scaling and rotation.
Analysis of algorithm : Existing block based algorithms are un-
ble to detect forgery if the copied region of the image has gone
48 B. Soni, P.K. Das and D.M. Thounaojam / Journal of Information Security and Applications 45 (2019) 44–51
Fig. 2. (a) Tampered image from MICC-F20 0 0 (b) first block match (c) second block match (d) Match forged region detected with outliers (e) Match forged region after
outliers removal (f) Localization of forgery in image.
Table 2
Different combinations of Rotation ( θ ) and Scaling ( S X , S y )
transformation applied on the MICC-F220 dataset Images.
Attack θ S X S y
A1 0 1 1
A2 10 1 1
A3 20 1 1
A4 30 1 1
A5 40 1 1
A6 0 1.2 1.2
A7 0 1.3 1.3
A8 0 1.4 1.4
A9 10 1.2 1.2
A10 20 1.4 1.4
Table 3
Computational time, TPR and FPR of the proposed
system using MICC-F220 dataset images for dif-
ferent block sizes.
Block size Time (in sec.) TPR% FPR%
4 × 4 9.2 97.1 8.2
8 × 8 8.8 97.55 8.4
12 × 12 8.4 98.2 9.4
16 × 16 7.8 98.5 11.4
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through rotation and scaling attacks. To resolve this issue, in this
work, initially, block matching is performed by comparing the rota-
tion and scaling invariant SURF descriptors of each block with the
remaining non-overlapping blocks. This matching gives the proba-
ble matched blocks. After that, to improve the accuracy of forgery
detection, a large block is formed by considering the eight neigh-
boring blocks of each SURF descriptor’s match block. In this large
block the probability of covering the complete forged area is high.
Fig. 2 describes the proposed algorithm. Fig. 2 (b) and 2 (c)
shows the large blocks formed after considering the eight neigh-
boring blocks of SURF descriptor matched blocks. Fig. 2 (d) shows
the matched MSER features which consist of some outliers. These
outliers are eliminated by performing affine transformation be-
tween matched features in Fig. 2 (e). Finally, localization of forgery
in image is given in Fig. 2 (f).
Performance of the proposed algorithm is quite satisfactory in
rotation and scaling transformation attacks. Fig. 3 shows the de-
tection results of MICC-F220 image in the presence of different at-
tacks. These results shows that the proposed algorithm is robust
against rotation and scaling as well as combined transformations.
Matching threshold in 2NN matching procedure is an useful pa-
rameter in accurately deciding the forgery. We analyzed the detec-
tion result for different values of threshold δ in the range from 0
to 1. In case of small values of δ, TPR rate is low and large values
of δ increases the false matches. Therefore, for the proposed algo-
rithm we consider δ= 0.6 which gives the optimum performance in
terms of TPR and FPR.
Table 2 describes the ten different (from A1 to A10) geomet-
ric transformation combinations applied on MICC-F220 dataset im-
ages. We evaluate the performance of the proposed algorithm in
MICC-F220 image set for the given geometric transformation in
Table 2 with respect to different δ values.
Average true positive rate (TPR) and false positive rate (FPR) for
all images corresponding to the geometric transformation with re-
spect to δ= 0.2, 0.4, 0.6, 0.8, 1.0 is given in Fig. 4 (a) and 4 (b) re-
spectively. From Fig. 4 it is clear that for attacks A9 and A10 the
performance of the system is degraded for all the threshold values
due to the nature of the transformation. Attacks A9 and A10 are o
omposite transformation (consisting of both rotation and scaling
ransformations; values shown in Table 2 ).
Quantitative results for MICC-F220 dataset is given in Table 3 . It
hows the processing time, TPR and FPR for different block sizes.
rom this table it is clear that if the size of the block is increasing,
he TPR is slightly increasing; On the other hand FPR is also in-
reasing. Therefore, in the proposed algorithm, block size of 8 × 8
s considered for optimum performance.
Detection results of the proposed algorithm is slightly lower
n MICC-F20 0 0 dataset because images of this dataset are of high
esolution and more complex than the ones in MICC-F220. Fig. 5
hows the qualitative results of the proposed method on images
f the MICC-F20 0 0 dataset. Copied region of Fig. 5 (c) image has
B. Soni, P.K. Das and D.M. Thounaojam / Journal of Information Security and Applications 45 (2019) 44–51 49
Fig. 3. Detection results on MICC-F220 dataset images (a) Without attack (b) Scaling attack (c) Rotation attack (d) Combined Rotation and Scaling attacks.
Fig. 4. MICC-F220 Dataset: Performance of the proposed system for different threshold values (a) TPR rate % versus different geometric transformations (b) FPR rate % versus
different geometric transformations.
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Table 4
Computational time, TPR and FPR for MICC-F20 0 0 dataset im-
ages of the proposed system for different block sizes.
Block size Time (in sec.) TPR% FPR%
4 × 4 21.12 92.1 9.2
8 × 8 20.8 96.4 9.8
12 × 12 19.4 97.2 11.4
16 × 16 18.2 97.4 14.2
T
f
one through 40 ° rotation. Copied region of Fig. 5 (d) image has
one through scaling transformation with scaling factors S x = 1 . 6
nd S y = 1 . 6 . Processing time, TPR and FPR of MICC-F20 0 0 dataset
mages for different block sizes are given in Table 4 . From these
uantitative results in can be observed that for large block sizes,
PR along with FPR is increasing. The proposed algorithm achieved
ptimum results for block size 8 × 8 i.e. TPR = 96.4(%) and FPR =.8(%) for MICC-F20 0 0 dataset images.
MICC-F600 dataset consists of high resolution images with
ore realistic and challenging tampering attacks. Therefore, TPR
ate of the proposed algorithm in MICC-F600 images is less
n comparison to MICC-F220 and MICC-F20 0 0 databases images.
able 5 shows the quantitative results of the proposed algorithmor MICC-F600 dataset images. Since MICC-F600 have different
50 B. Soni, P.K. Das and D.M. Thounaojam / Journal of Information Security and Applications 45 (2019) 44–51
Fig. 5. Detection results for MICC-F20 0 0 dataset images (a) Without attack (b) Without attack (c) Rotation attack (d) Scaling attack.
Algorithm 1 Proposed algorithm.
Input : F orged image, Img.
Output : F orged region
procedure Copy_move ( Img)
[ M, N scale ] ← size (Img)
if scale > 1 then
I ← 0 . 2989 R + 0 . 587 G + 0 . 114 B � R, G and B are color
components
else
I ← Img
end if
B ← block _ part it ion (I, block _ size )
for x = 1 toη − 1 do � η is the total number of blocks
for y = x + 1 toη do
S 1 ← SURF _ f eature (block x )
S 2 ← SURF _ f eature (block y )
Mat ch _ SURF ← mat ch _ f eat ure (S 1 , S 2 ) � using 2NN
procedure
if Match_SURF > δ then
S 11 ← block _ neighbour(S 1 ) � creates a bigger block
S 22 ← block _ neighbour(S 2 )
M 1 ← detect _ MSER (S 11 ) � extract MSER regions
M 2 ← detect _ MSER (S 22 )
D 1 ← extract descriptor of M 1
D 2 ← extract descriptor of M 2
M atch _ M SER ← match _ descr iptor (D 1 , D 2 ) � using
2NN procedure
Match ← remov e _ outliers (M atch _ M SER ) � using
affine transform
if Match ≥ 1 then
Display the f orged region
else
Discard the blocks
end if
end if
end for
end for
end procedure
Table 5
Computational time, TPR and FPR for MICC-F600
dataset images of the proposed system for differ-
ent block sizes.
Block size Time (in sec.) TPR% FPR%
4 × 4 25.2 88.2 9.2
8 × 8 23.1 90.4 10.8
12 × 12 21.4 91.2 13.4
16 × 16 19.3 92.6 15.2
Table 6
Performance comparison of proposed method with
existing methods in terms of TPR, FPR values (%) and
Computational time.
Method FPR TPR Time
(%) (%) (in sec)
Fridrich et al. [12] 84 89 294.69
Popescu et al. [23] 86 87 70.97
Bo et al. [8] 3.64 73.84 2.85
Li et al. [24] 8.86 91.55 14.45
Yang et al. [25] 9.02 95.88 10.20
Zhong et al. [18] 14.82 93.75 22.40
Yang et al. [26] 10.42 95.45 12.40
Proposed method 8.40 97.55 8.80
s
l
i
p
t
f
g
a
m
p
s
ized images, so average computational time per image is calcu-
ated for different block sizes as shown in Table 5 .
Performance comparison of the proposed method with exist-
ng methods in terms of TPR, FPR and average computational time
er image is given in the Table 6 . These methods are tested on
he MICC-F220 database images. It can be observed that the per-
ormance of the proposed method is superior to all the methods
iven in Table 6 in all aspects except method [8] . In terms of FPR
nd computational time method [8] is better than the proposed
ethod but TPR of the method is low. Therefore, it seems that the
erformance of proposed method is quite satisfactory while con-
idering all these aspects.
B. Soni, P.K. Das and D.M. Thounaojam / Journal of Information Security and Applications 45 (2019) 44–51 51
6
b
f
a
f
p
d
g
S
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s
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p
m
t
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M
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[
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[
[
[
[
. Conclusions and future scope
In this paper, we have proposed an enhancement of the block
ased copy-move forgery detection technique using hybrid local
eatures. The proposed technique is blind, since only tampered im-
ge is given for detection of copy-move region. Matching is per-
ormed in between the blocks of tampered image; in the pro-
osed technique, original image is not considered for copy-move
etection. To improve robustness in the forgery detection, large re-
ions are formed by considering eight neighboring blocks of each
URF features match blocks. MSER region is detected from each
arge regions and then extracted SURF descriptors are compared
or matching. Finally, affine transform is applied to remove the out-
iers. Time complexity of SURF is decreased due to the use of in-
egral image and Hessian matrix in interest points detection. De-
criptor dimension of SURF is lesser than SIFT. Therefore, matching
f SURF descriptor is faster than that of SIFT. Results show that the
roposed method perform well in the presence of different geo-
etric attacks like rotation, scaling and composition of these at-
acks, where majority of block based algorithms did not perform
ell. We also test the robustness of the proposed method using the
ICC-F20 0 0 and MICC-F600 datasets where high resolutions and
hallenging tampered images are present. Performance comparison
f the proposed method with seven existing methods show that
his proposed method outperformed the existing methods. Some
mprovement is needed in the algorithm to detect multiple forg-
ries present in the image and detection of accurate forgery in
ighly similar regions.
upplementary material
Supplementary material associated with this article can be
ound, in the online version, at doi: 10.1016/j.jisa.2019.01.007 .
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