COPY-ROTATE-MOVE FORGERY DETECTION BASED ON SPATIAL

DOMAIN

Sondos M. Fadl, Noura A. Semary, and Mohiy M. HadhoudFaculty of Computers and Information, Menofia University, Egypt

{sondos.magdy,noura.samri,mmhadhoud}@ci.menofia.edu.eg

IntroductionRelated worksObjectives Proposed methodExperiment resultsConclusions and future work

AGENDA

INTRODUCTION

As image is better than thousands of words, World Wide Web nowadays contains a large amount of digital images used for effective communication process.

It becomes very trivial for professionals or non-professionals to edit any pre-existing photographs by using freely available image editing tools, such as Photoshop.

INTRODUCTION

INTRODUCTION

Image Forgery

Types:

Copy-Move Image Compositing

Image Enhancement

INTRODUCTION1 -Copy-Move (CM) forgery :

CM image tampering is one of the frequently used techniques.The most performed operations in (CM) forgery are either hiding a region in the image, or adding a new object into the image .

The left image was captured in 1930 where Nikolai Yezhov, was walking with Stalin. Following his execution in 1940

Yezhov was removed from all Stalin photos !!

INTRODUCTION2 -Image compositing :

that mixes between two or more different images.3 -Image enhancement:

such as blurring, contrast or brightness alteration etc.

Example of image compositing.Example of image enhancement.

INTRODUCTION

Image Forgery Detection Techniques:

Active Methods

Passive Methods “Blind”

1 -Active methods such as watermarking depend on prior information about the original image that in many cases is not available.

2 -Passive or blind methods not depend on prior information about the original image, it needs only forgery image.

INTRODUCTION

J. Fridrich et al. (2003) suggested one of the raw and earliest methods to detect copy move forgery.

1 -Blocking 2 -Pixel value comparison

This method is exact match ,It detects the exact duplication of region .exact match is hard to find any manipulation like blurring and JPEG.

RELATED WORKS

Y. Huang et al. (2011) : Another method called robust match is suggested in which instead of pixel value comparison “exact match” quantized DCT coefficients are matched.

This method can detect type of manipulations such as JPEG compression and Gaussian blurring. However the above method fails for any type of geometric transformations of the block such as rotation, scaling etc.

RELATED WORKS

RELATED WORKSH. J. Lin et al. (2009) : suggested a method using subblocking for feature extraction.It take nine features for each block as below:

Shift vector u(i)=P(+1)-P() is used to detect the duplicated regions.

Block

RELATED WORKS

G. Lynch et al. (2013) : 1 -Using average gray

value as a feature for each block.

2 -blocks are sorted .2 -Manual grouping for

collecting similar blocks in a same bucket for reduce processing time.

OBJECTIVES

Parallel block matching

Average of Nested frames

Rotation detection

Accelerated

we propose method which is efficient and fast for detecting Copy-Move regions even when the copied region was undergone rotation modify in spatial domain. We named Copy-Rotate-Move Forgery Detection based on Spatial Domain (CRMS) .

PROPOSED METHOD

CM forgery detection consists of basic steps :

PROPOSED METHOD

Preparing Feature

ExtractionMatching and

decision

Step 1

Step 2

Step 3

PROPOSED METHODWe detect the duplicated regions by Block Matching strategy, where the image is dividing into equal-size overlapped blocks, then each block is matched with all other possible blocks in the same image .

Features are extracted from each block by dividing it to nested frames and calculate the average of each frame .

CRMS

Input image with size MxN

 

Gray scale

conversion 

Dividing into

overlapping blocks

 

Features extraction

 

Clustering blocks for K

classes

 

 

lexicographically sorted for

blocks in each class

Logical distance calculation

Physical distance calculation

Decision

Preparing

CRMS FLOWCHART

1 .Preparing Stage :If the input image is RGB, it converts the image into the corresponding gray scale version and divide into blocks.For an image of size M×N, the image could be divided into small overlapping blocks of b×b pixels resulting in B blocks where:

CRMS STAGES

CRMS STAGES2 .Features extraction :

If a block has been rotated by basics angles (90, 180 and 270), note the following, lack of change in the values of block, but values in each frame have been shifted in the same frame.

(a( )F) (c( )d)Example of F rotation: (a) Original block, (b) rotate

angle , (c) rotate angle and (d) rotate angle

3 2 16 5 49 8 7

9 6 38 5 27 4 1

7 8 94 5 61 2 3

1 4 72 5 83 6 9

CRMS STAGESFeatures are extracted from each block as the averages of the frames .

Feature vector contains coefficients as well as 2 indices for block position, it computed below:

3 .K-means Clustering : We used cluster technique to clustering blocks to many class for parallel comparison to reduce processing time .

K-means algorithm is considered a fast clustering that groups similar blocks based on features into K number of groups. we used Fast K-Means algorithm (FKM), that proposed by Elkan (2003).

CRMS STAGES

F 9 F 8 F 7 F 6 F 5 F 4 F 3 F 2 F 1

CRMS STAGES

F 9 F 8 F 7 F 6 F 5 F 4 F 3 F 2 F 1

(Class 1 )

F4, F1, F6

(Class 2 )

F3, F2, F5, F8

 

(Class 3 )

F7, F9

 

Sorting F in each class

Applying FKM

Block 9

Block 8

Block 7

Block 6

Block 5

Block 4

Block 3

Block 2

Block 1

Extracting Features

CRMS STAGES4 .Matching:

Assume saving the sorted matrix in As, then each row is compared to . Logical distance between the two

feature vectors is calculated below :

If is less than a threshold T, then two blocks are supposed to similar.

Physical distance is tested below to eliminate the false positives:

where (, ) is the position of and ( , ) is the position of. When is greater or equal than a threshold , mark the regions in the result image .

CRMS STAGES

Experiment method and procedure :The experiments were carried out on the Matlab R2012a, RAM 4 GB and processor 2.30 GHZ .

All the images were 128×128 pixels gray image saved in BMP format .

All the parameter in the experiment were set as: b=9 ,T=0.2 , Nd=16 , L=9 and K={4,10,20} .

EXPERIMENT RESULTS

This figure presents the results of detecting tampered images without any distortion operations, each row content four images original, tampered, clustering and detection result image that content duplicated regions from left to right respectively.

VISUAL RESULT

This figure shows the detection result of rotation angle is 90°.

VISUAL RESULT

This figure shows the detection result of rotation angle is 180°.

VISUAL RESULT

This figure shows the detection result of rotation angle is 270°.

VISUAL RESULT

This figure shows the detection result of horizontal reflection.

VISUAL RESULT

This figure shows the detection result of vertical reflection.

VISUAL RESULT

More detected results over tampered images with somemodifications shown in the figure , that shows in first row original image, detected result with Gaussian blur in second row and in thread row the detected result over JPEG compressed with QF=70.

VISUAL RESULT

EXPERIMENT RESULTS

Time(s)Lynch (2013) Huang (2011) Tripathi (2011) CRMS

7.68 4.7005 6.4018 1.5237

The performance time of different methods

shows the performance time of CRMS compared to other methods .

Note that, the proposed method decreased the processing time up to 70% faster .

Copy-move images

 Different modificationsNumber of images

Detection rate

Without modification 100 99.9%

Rotate with basic angles 100 99.5%Gaussian

blur 50 90%JPEG

compression QF=100

50 70%

JPEG compression

QF=9050 64%

JPEG compression

QF=7050 58%

The detection rate for different modifications

Modifications

 Different methods

 G. Lynch (2013)

 Y. Huang

(2011)

 CRMS

Without modificatio

n97% 99.9% 99.9%

Rotation 0% Only less than 5° 99.5%

Gaussian blur 30% 90% 90%

JPEG compressi

on30% 80% 70%

The performance rate for different methods

 Thresho

ld

Number of blocks

Detection

True Positive

False Positive

0.1 1336 1336 (100%) 0( 0.00%)

0.2 1338 1336 (99.85%) 2( 0.14%)

0.3 1340 1336 (99.70%) 4( 0.29%)

0.4 1343 1336 (99.47%) 7( 0.52%)

EXPERIMENT RESULTSShow the result for different Thresholds: up row shows original image and copy-Rotate-Move image from left to right respectively and down row shows result with T=0.1, T=0.2, T=0.3 and T=0.4.

In this paper, we have proposed a fast and efficient method for CM forgery detection whether without modification and with rotation modify, by using Fast K-means and block frame features.

The experiment results show that the proposed method has the ability to detect CM and CRM forgery in an image faster than other systems by about 75%.

The method is to be improved for detecting duplicated region under the rotation with any angle, and detecting CM with scale modification.

CONCLUSIONS AND FUTURE WORK

A. Khan, S. A. Malik, A. Ali, R. Chamlawi, M. Hussain, M. T. Mahmood, et al, "Intelligent reversible watermarking and authentication: hiding depth map information for 3D cameras" , Elsevier Information Sciences, vol. 216, pp. 155-175, 2012.

J. H. Hsiao, C. S. Chen, L. F. Chien, and M. S. Chen, "A new approach to image copy detection based on extended feature sets." IEEE Image Processing, vol. 16, pp. 2069-2079, no. 8, 2007.

H. Ling, F. Zou, W. Q. Yan, Q. Ma, and H. Cheng, "Efficient image copy detection using multiscale fingerprints", IEEE Multimedia, 2012.

S. Nikolopoulos, S. Zafeiriou, N. Nikolaidis and I. Pitas, "Image replica detection system utilizing R-trees and linear discriminant analysis." Elsevier Pattern Recognition, vol. 43, pp. 636-649, no. 3, 2010.

V. Christlein, C. Riess, J. Jordan, C. Riess and E. Angelopoulou, "An evaluation of popular copy-move forgery detection approaches." IEEE Information Forensics and Security, vol. 7, pp. 1841-1854, no. 6, 2012.

A. J. Fridrich, F. D. Soukal, and A. J. Lukas, "Detection of copy-move forgery in digital images." in Proceedings of Digital Forensic Research Workshop, 2003.

G. Lynch, F. Y. Shih and H. Y. M. Liao, "An efficient expanding F algorithm for image copy-move forgery detection." Elsevier Information Sciences, vol. 239, pp. 253-265, 2013.

REFERENCES

Y. Huang, W. Lu, W. Sun and D. Long, "Improved DCT-based detection of copy-move forgery in images." Elsevier Forensic science international, vol. 206, pp. 178-184, no. 1, 2011 .

H. J. Lin, C. W. Wang and Y. T. Kao, "Fast copy-move forgery detection." WSEAS Transactions on Signal Processing (World Scientific and Engineering Academy and Society), vol. 5, pp. 188-197, no. 5, 2009.

V. K. Singh and R. C. Tripathi, "Fast and efficient region duplication detection in digital images using sub-blocking method." International Journal of Advanced Science and Technology, vol. 35, pp. 93-102, 2011.

A. C. Popescu and H. Farid, "Exposing digital forgeries by detecting duplicated image regions." Dept. Comput.er Sci., Dartmouth College, Tech. Rep. TR2004-515, 2004.

C. Elkan, "Using the triangle inequality to accelerate k-means." ICML. Pp. 147-153, 2003.

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T. T. Ng, J. Hsu, S. F. Chang, Columbia Image Splicing Detection Evaluation Dataset.: http://www.ee.columFia.edu/ln/dvmm/downloads/AuthSplicedDataSet/AuthSplicedDataSet/

REFERENCES