Image Processing Reference
In-Depth Information
Fig. 7.1
One dimensional cellular automata with three neighborhoods for cell
X
Rule 1:
Cell[
X
](
t
+1) = Cell[
X
-1] (
t
)(OR)Cell[
X
+1] (
t
)
Rule 2:
Cell[
X
](
t
+1) = Cell[
X
-1] (
t
) (AND) Cell[
X
+1] (
t
)
As long as we consider Rule 1, and the input sequence equals to 01100, then
output sequence will be 11110. Table 7.1 shows the output of this cellular automata
usinganORlocalrule.
By using Rule 2, while the input sequence equals to 01110, then output sequence
will be 00100 (Table 7.2).
In this scenario, we propose a digital image forgery detection algorithm based on
the cellular automata and LU decomposition. Experiments for this scenario will be
described in detail in Section 7.4.
7.2.1
Proposed Model
The main idea of this proposed algorithm is to protect a digital image against forgery
by creating and embedding an unpredictable cipher key into the spatial domain of
an image. We embed the bit sequence of the cipher key into the LSB (Least Signif-
icant Bit) of the particular pixels in the original image because it takes less time on
embedding. Section 7.4 shows some experimental validations obtained from embed-
ding into different places. Using LSB decreases time consumption and may cause
to reduce sensitivity to some attacks.
Our proposed algorithm performs on a grayscale images and generates a .png file
including lossless PNG image with the grayscale model. We generate PNG image
because generating any other formats like JPEG image would result into losing se-
cret key embedded into pixel's LSB. The input type is not important in the proposed
method and it may perform the same process on the different types of digital images
such as RGB or CMYK, and different formats like .bmp, .gif, .pgm and etc. For
Ta b l e 7 . 1
An example of cellular automata
Cell Number
0
1
2
3
4
Input Sequence (time
t
)
0
1
1
0
0
Cell[X]
t+1
= Cell[X-1]
t
(OR) Cell[X+1]
t
Cellular Automata Rule
Output Sequence (time
t
+
1)
1
1
1
1
0
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