we propose a method which is efficient and fast for detecting Copy-Move regions even when the copied region was undergone rotation modify in spatial domain.

1. COPY-ROTATE-MOVE FORGERY
DETECTION BASED ON SPATIAL
DOMAIN
Sondos M. Fadl, Noura A. Semary, and Mohiy M. Hadhoud
Faculty of Computers and Information, Menofia University, Egypt
{sondos.magdy,noura.samri,mmhadhoud}@ci.menofia.edu.eg

2. Introduction
Related works
Objectives
Proposed method
Experiment results
Conclusions and future work
AGENDA

3. 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.

4. 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

5. INTRODUCTION
Image Forgery
Types:
Copy-Move
Image
Compositing
Image
Enhancement

6. INTRODUCTION
1- 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 !!

7. INTRODUCTION
2- 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.

8. INTRODUCTION
Image Forgery Detection
Techniques:
Active Methods
Passive Methods
“Blind”

9. 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

10. 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

11. 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

12. RELATED WORKS
H. J. Lin et al. (2009) : suggested a method using subblocking
for feature extraction.
It take nine features for each block as below:
𝑓𝑖 =
𝑓𝑖 = 𝐴𝑣𝑒 𝐵 𝑖𝑓 𝑖 = 1,
𝐴𝑣𝑒(𝑆𝑖 − 1)/4 𝐴𝑣𝑒(𝐵) + 𝜀1 𝑖𝑓 2 ≤ 𝑖 ≤ 5,
𝑓𝑖 = 𝐴𝑣𝑒 𝑆𝑖 − 5 − 𝐴𝑣𝑒 𝐵 𝑖𝑓 6 ≤ 𝑖 ≤ 9.
Shift vector u(i)=P(𝐵𝑖+1)-P(𝐵𝑖) is used to detect the duplicated
regions.
Block

13. 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.

14. OBJECTIVES
Parallel block
matching
Average of
Nested frames
Rotation
detection
Accelerated

15. 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

16. CM forgery detection consists of basic steps:
PROPOSED METHOD
Preparing
Feature Extraction
Matching and decision
Step
1
Step
2
Step 3

17. PROPOSED METHOD
We 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.

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

19. 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

20. 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:
𝐵 = (𝑀 − 𝑏 + 1) × (𝑁 − 𝑏 + 1)
CRMS STAGES

21. CRMS STAGES
2. 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 𝟗𝟎°, (c)
rotate angle 𝟏𝟖𝟎° and (d) rotate angle 𝟐𝟕𝟎°
321
654
987
963
852
741
789
456
123
147
258
369

22. CRMS STAGES
Features are extracted from each block as the averages of the
frames.
Feature vector contains 𝑛𝑢𝑚 = (
𝑏−1
2
+ 1) coefficients as well
as 2 indices for block position, it computed below:
𝑣𝑖 =
𝑣𝑖 = 𝐴𝑣𝑒 𝐵 𝑖𝑓 𝑖 = 1,
𝑣𝑖 = 𝐴𝑣𝑒 𝐹𝑗 𝑖𝑓 𝑖 = 2 ≤ 𝑖 ≤ 𝑛𝑢𝑚

23. 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

24. F 9F 8F 7F 6F 5F 4F 3F 2F 1
CRMS STAGES
F 9F 8F 7F 6F 5F 4F 3F 2F 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

25. CRMS STAGES
4. Matching:
Assume saving the sorted matrix in As, then each row As(i) is
compared to 𝐴𝑠(𝑖+1). Logical distance between the two feature
vectors is calculated below:
𝑑𝑖𝑓 = 𝑗=1
𝑙
𝐴𝑠𝑖
𝑗
− 𝐴𝑠𝑖+1
𝑗
If 𝑑𝑖𝑓 is less than a threshold T, then two blocks are supposed
to similar.

26. Physical distance is tested below to eliminate the false
positives:
𝑑𝑖𝑠
= (𝐴𝑠𝑖
L+1
− 𝐴𝑠𝑖+1
L+1
)2+(𝐴𝑠𝑖
L+2
− 𝐴𝑠𝑖+1
L+2
)2
where (𝐴𝑠𝑖
L+1
, 𝐴𝑠𝑖
L+2
) is the position of 𝐵𝑖 and (𝐴𝑠𝑖+1
L+1
, 𝐴𝑠𝑖+1
L+2
)
is the position of 𝐵𝑖+1.
When 𝑑𝑖𝑠 is greater or equal than a threshold 𝑁𝑑, mark the
regions in the result image.
CRMS STAGES

27. 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

28. 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

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

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

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

32. This figure shows the detection
result of horizontal reflection.
VISUAL RESULT

33. This figure shows the detection
result of vertical reflection.
VISUAL RESULT

34. More detected results over
tampered images with some
modifications 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

35. 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.

36. Copy-move
images
Different modifications
Number 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=90
50 64%
JPEG
compression
QF=70
50 58%
The detection rate for different
modifications
Modification
s
Different methods
G. Lynch
(2013)
Y. Huang
(2011)
CRMS
Without
modification
97% 99.9% 99.9%
Rotation 0%
Only less
than 5°
99.5%
Gaussian
blur
30% 90% 90%
JPEG
compression
30% 80% 70%
The performance rate for
different methods

37. Threshol
d
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 RESULTS
Show 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.

38. 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

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