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: badal@cse.nits.ac.in (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-

�x

46 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 algorithm

or 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

l

f

l

t

s

o

p

m

t

w

M

c

o

t

i

e

h

S

f

R

[

[

[

[

[

[

. 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 .

eferences

[1] Lowe DG . Distinctive image features from scale-invariant keypoints. Int J Com-

put Vis 2004;60:91–110 . [2] Mohamadian Z, Pouyan AA. Detection of duplication forgery in digital images

in uniform and non-uniform regions. In: International conference on computermodelling and simulation; 2013. p. 455–60. doi: 10.1109/UKSim.2013.94 .

[3] Warif NBA , Wahab AWA , Idris MYI , Ramli R , Salleh R , Shamshirband S ,

et al. Copy-move forgery detection: survey, challenges and future directions.J Netw Comput Appl 2016;75:259–78 .

[4] Soni B , Das PK , Thounaojam DM . CMFD: A detailed review of block based andkey feature based techniques in image copy-move forgery detection. IET Image

Proc 2018;12(2):167–78 . [5] Huang H , Guo W , Zhang Y . Detection of copy-move forgery in digital images

using sift algorithm. In: IEEE Pacific-Asia workshop on computational intelli-

gence and industrial application, 2; 2008. p. 272–6 .

[6] Ardizzone E, Bruno A, Mazzola G. Detecting multiple copies in tampered im-ages. In: IEEE international conference on image processing; 2010. p. 2117–20.

doi: 10.1109/ICIP.2010.5652490 . [7] Amerini I , Ballan L , Caldelli R , Bimbo AD , Serra G . A sift-based forensic method

for copy-move attack detection and transformation recovery. IEEE Trans InfForensics Secur 2011;6(3):1099–110 .

[8] Bo X, Junwen W, Guangjie L, Yuewei D. Image copy-move forgery detectionbased on SURF. In: International conference on multimedia information net-

working and security; 2010. p. 889–92. doi: 10.1109/MINES.2010.189 .

[9] Mishra P , Mishra N , Sharma S , Patel R . Region duplication forgery detectiontechnique based on SURF and HAC. Int J Adv Comput Technol 2013;6(17):1–8 .

[10] Ulutas G , Muzaffer G . A new copy move forgery detection method resis-tant to object removal with uniform background forgery. Math Problem Eng

2016:1–19 . [11] Zheng J , Liu Y , Ren J , Zhu T , Yan Y , Yang H . Fusion of block and keypoints based

approaches for effective copy-move image forgery detection. Multidimens Syst

Signal Process 2016;27(4):989–1005 . [12] Fridrich J , Soukal D , Lukas J . Detection of copy-move forgery in digital images.

In: Digital forensic research workshop; 2003. p. 55–61 . [13] Qin C , Ji P , Zhang X , Dong J , Wang J . Fragile image watermarking with pix-

el-wise recovery based on overlapping embedding strategy. Signal Process2017;138:280–93 .

[14] Fattah SA, Ullah MMI, Ahmed M, Ahmmed I, Shahnaz C. A scheme for copy-

move forgery detection in digital images based on 2D-DWT. In: InternationalMidwest Symposium on Circuits and Systems (MWSCAS); 2014. p. 801–4.

doi: 10.1109/MWSCAS.2014.6908536 . [15] Lee J-C , Chang C-P , Chen W-K . Detection of copy-move image forgery using

histogram of orientated gradients. Inf Sci 2015;321(C):250–62 . [16] Qin C , Chen X , Ye D , Wang J , Sun X . A novel image hashing scheme with per-

ceptual robustness using block truncation coding. Inf Sci 2016;361:84–99 .

[17] Bi X , Pun C-M , Yuan X-C . Multi-level dense descriptor and hierarchical featurematching for copymove forgery detection. Inf Sci 2016;345:226–42 .

[18] Zhong J , Gan Y , Young J , Huang L , Lin P . A new block-based method for copymove forgery detection under image geometric transforms. Multimed Tools

Appl 2017;76(13):14887–903 . [19] Soni B, Das PK, Thounaojam DM. Copy-move tampering detection based on

local binary pattern histogram fourier feature. In: International conference on

computer and communication technology. ACM; 2017. p. 78–83. doi: 10.1145/3154979.3155001 .

20] Soni B, Das PK, Thounaojam DM. Blur invariant block based copy-move forgerydetection technique using FWHT features. In: International conference on wa-

termarking and image processing. ACM; 2017. p. 22–6. doi: 10.1145/3150978.3150987 .

[21] Matas J, Chum O, Urban M, Pajdla T. Robust wide baseline stereo from max-

imally stable extremal regions. In: The British machine vision conference.BMVA Press; 2002. p. 36.1–36.10. ISBN 1-901725-19-7 . Doi: 10.5244/C.16.36 .

22] Amerini I , Ballan L , Caldelli R , Bimbo AD , Tongo LD , Serra G . Copy-move forgerydetection and localization by means of robust clustering with j-linkage. Signal

Process Image Commun 2013;28:659–69 . 23] Popescu AC , Farid H . Exposing digital forgeries by detecting traces of resam-

pling. IEEE Trans Signal Process 2005;53:758–67 . 24] Li J , Li X , Yang B , Sun X . Segmentation-based image copy-move forgery detec-

tion scheme. IEEE Trans Inf Forensics Secur 2015;10:507–18 .

25] Yang B , Sun X , Guo H , Xia Z , Chen X . A copy-move forgery detection methodbased on cmfd-sift. Multimed Tools Appl 2017:1–19 .

26] Yang F , Li J , Lu W , Weng J . Copy-move forgery detection based on hybrid fea-tures. Eng Appl Artif Intell 2017;59:73–83 .