Journal of Computer and Communications, 2014, 2, 51-57
Published Online March 2014 in SciRes. http://www.scirp.org/journal/jcc
http://dx.doi.org/10.4236/jcc.2014.24008
How to cite this paper: Wang, Q., Li, Z.H., Zhang, Z.Z. and Ma, Q.L. (2014) Video Inter-Frame Forgery Identification Based
on Consistency of Correlation Coefficients of Gray Values. Journal of Computer and Communications, 2, 51-57.
http://dx.doi.org/10.4236/jcc.2014.24008
Video Inter-Frame Forgery Identification
Based on Consistency of Correlation
Coefficients of Gray Values
Qi Wang, Zhaohong Li, Zhenzhen Zhang, Qinglong Ma
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijin g, China
Received October 2013
Abstract
Identifying inter-frame forgery is a hot topic in video forensics. In this paper, we propose a me-
thod based on the assumption that the correlation coefficients of gray values is consistent in an
original video, while in forgeries the consistency will be destroyed. We first extract the consisten-
cy of correlation coefficients of gray values (CCCoGV for short) after normalization and quantiza-
tion as distinguishing feature to identify inter-frame forgeries. Then we test the CCCoGV in a large
database with the help of SVM (Support Vector Machine). Experimental results show that the pro-
posed method is efficient in classifying original videos and forgeries. Furthermore, the proposed
method performs also pretty well in classifying frame insertion and frame deletion forgeries.
Keywords
Inter-Frame Forgeries; Content Co nsis tency; Video Forensics
1. Introduction
Nowadays with the ongoing development of video editing techniques, it becomes increasingly easy to modify
the digital videos. How to identify the authenticity of videos has become an important field in information secu-
rity.
Video forensics aims to look for features that can distinguish video forgeries from original videos. Thus
people can identify the authenticity of a given video. A kind of distinguishing method which is based on video
content and composed of copy-move detection and inter-frame tampering detection becomes a hot topic in video
forensics. Chih-Chung et al. [1] used correlation of noise residue to locate forged regions in a video. Leida et al.
[2] presented a method to detect the removed object in video by employing the magnitude and orientation of
motion vectors. A.V. et al. [3] detected the spatial and temporal copy-paste tampering based on Histogram of
Oriented Gradients (HOG) feature matching and video compression properties. For inter-frame forgeries, such
as frame insertion and frame deletion forgeries, some effective methods were proposed [4,5]. Juan et al. [4] pre-
sented a model based optical flow consistency to detect video forgery as inter-frame forgery will disturb the
optical flow consistency. Tianqiang et al. [5] proposed a method using gray values to represent the video content

Q. Wang et al.
52
and detected the tampered video including frame deletion, frame insertion and frame substitution.
In this paper, we propose a simple yet efficient method based on consistency of correlation coefficients of
gray values (CCCoGV for short in the following text) to distinguish inter-frame forgeries from original videos.
The basic idea of our method is that differences of correlation coefficients of gray values of original sequences
are consistent while differences of inter-frame forgeries will have abnormal points. We first calculate the differ-
ences of correlation coefficients of gray values between sequential frames of videos, and then use Support Vec-
tor Machine (SVM) [6] to classify original videos and inter-frame forgeries. We test our method on a large da-
tabase, and results show that it is a method of high detection accuracy.
2. Review of Content Continuity
In a video which has not been tampered, the correlation of the content between the adjacent frames is high,
while the content correlation between the far apart frames is relatively low. Gray value of frame can be a good
representative of video content, which reflect the distribution and characteristics of the color and brightness lev-
el of an image. Based on this idea, a method was proposed to find out which frames has been tampered in a vi-
deo by using gray value to depict the video content and the CCCoGV to describe whether video content is conti-
nuous or not [5].
The framework and details of the method [5] is as shown in Figure 1.
The main steps are as follows:
1) Decompose the video into orderly image sequence. Video is actually the orderly sequence of the 2-D image
in the 1-D time domain. To extract the content features of a video, we can extract the features of the orderly im-
age sequence.
2) Transform the RGB color image into gray image. The required storage space of a RGB color image is three
times larger than that of a gray image. To reduce the amount of calculation, we use gray image to represent the
content of the real color image. In the YUV model, the physical meaning of Y component is brightness of point.
The brightness value represents the gray value of image. According to the relationship between RGB model and
YUV model, brightness Y can be represented by the R\G\B components as
0.29890.58700.1140 .Y RGB=×+×+×
(1)
3) Calculate inter-frame correlation coefficient between adjacent frames. A gray image can be stored as a sin-
gle matrix. Each element of the matrix corresponds to one image pixel and represents a different gray level. Eq-
uation (2) defines the correlation coefficient between two frames.
11
2
2
11
((, ))((, )),
(((,)) )(((,)) )
1, 2,,1
ijrara rara
k kkk
k
ij ij ra rarara
kkk k
F ij FFij F
rF ij FFij F
kn
++
++
−−
∑∑
=
−−
∑∑ ∑∑
= −

( 2)
where,
k
r
is the correlation coefficient between frame k and frame k + 1.
( )
,
k
ra
F ij
represents the gray value
of the pixel which is in the row i and column j of the frame k.
k
ra
F
corresponds to the average value of the gray
values
( )
,
k
ra
F ij
. Therefore, from a continuous frame sequence of which the length is n, we can obtain a corre-
lation sequence of
1 n−
.
4) Calculate the difference between correlation coefficients of adjacent frames. Equation (3) defines the dif-
ference.
Video input
Video inputVideo into images
Video into imagesColor image into gray
image
Color image into gray
image
Calculating inter-frame
correlation coefficients
between adjacent frames
Calculating inter-frame
correlation coefficients
between adjacent frames
Calculating the
difference of correlation
coefficients
Calculating the
difference of correlation
coefficients Outlier detection
Outlier detection
Video was
composited by a
continuous image
sequence. Video
and images are
equivalent in
content.
Video was
composited by a
continuous image
sequence. Video
and images are
equivalent in
content.
Between adjacent frames
the time interval is short
and the correlation is
high. While between the
far apart frames the time
interval is long and the
correlation is low.
Between adjacent frames
the time interval is short
and the correlation is
high. While between the
far apart frames the time
interval is long and the
correlation is low.
The inter-frame
correlation of the video
without tampering must
be continuous.
The inter-frame
correlation of the video
without tampering must
be continuous.
Use the Chebyshev
inequality twice.
Use the Chebyshev
inequality twice.
Reduce the amount of
computation.
RGB color
image(256×256×256)
Gray image(256)
Reduce the amount of
computation.
RGB color
image(256×256×256)
Gray image(256)
Figure 1. The framework and details of the method [5].

Q. Wang et al.
53
1
,2
0, 1
kk
k
rr k
rk
+
−≥
∆=
=

(3 )
By the equation, we can obtain a sequence
123432 1
{,,,,,,,}
k nnnn
r rrrrrrr
−−−−
∆=∆∆∆…∆∆∆∆
to represents the
variability of the content of a whole video. As we supposed, the variability of the video without tampering
should be consistent, otherwise, the consistency will be destroyed by the deleted or inserted frames.
5) Finally, use the Chebyshev inequality twice to find out abnormal points which indicates where the tam-
pered frames are.
Digital video forgeries detection based on content continuity is efficient. Based on this method, we can detect
which frames were tampered in a video. But we cannot identify whether a video has been tampered by the me-
thod [5].
3. Proposed Method
3.1. Analysis of CCCoGV for Original Videos and Inter-Frame Forged Videos
We proposed a method based on CCCoGV to classify original videos and inter-frame forgeries. Gray value is
efficient in depicting video content. For a frame-tampered video, the gray values would be changed a lot at the
tampered point.
In the original video database, every frame is non-tampered. The continuous frames in the original video are
as shown in Figure 2. These three frames are continuous in a video. We can see the motion of the people is
small and the background almost has no difference. In fact, a whole video is composed by hundreds of frames.
The fluctuation of differences between correlation coefficients of adjacent frames is shown in Figure 3. From
Figure 3, we can see that the differences of sequential frames are almost stable with values which are a bit larg-
er than zero. That is to say, differences of correlation coefficients of original frames are consistent. The results
are square with the consensus that for videos with fast motion, the correlation coefficient between continuous
frames is low, but the change of correlation coefficients is stable.
Videos in the 25-frame-inserted video database and 100-fra me-inserted video database are inserted 25 or 100
frames somewhere. Figure 4 shows four frames gotten from a 100-frame-inserted video. The first and forth im-
ages were the adjacent frames before frame insertion. We insert 100 frames between them then. And the inserted
Figure 2. Three continuous frames from an original video.
Figure 3. The CCCoGV of original video.
0100 200 300 400 500
0
0.005
0.01
0.015
0.02
0.025
0.03
The Difference Between Correlation Coefficients

Q. Wang et al.
54
100 frames are continuous. The second and third images are the first and last frames of the video which was in-
serted to the original video. In a whole 100-frame-inserted video, the fluctuation of differences between correla-
tion coefficients of adjacent frames is shown in Figure 5. From Figure 5 we can see two pairs of peaks, which
represent two tampered points. At every tampered point, a pair of peaks appears. The front pair of peaks denotes
the start point of the insertion, while the latter pair of peaks represents the end point. The fluctuations in these
two places are much larger than that of other points.
Similar to the frame-inserted video, in the 25-frame-deleted video database and 100-frame-deleted video da-
tabase, every video is deleted 25 or 100 frames somewhere. Figure 6 shows three frames gotten from a 100-
frame-deleted video. The first two images are the frames before frame deletion and the third image is the frame
after deleting 100 frames from the original video. In fact, we cannot find much difference between the last two
images. In a whole 100-frame-deleted video, the fluctuation of differences between correlation coefficients of
adjacent frames is shown in Figure 7. From Figure 7 we can see one pair of peaks, which represent a tampered
point. The pair of peaks denotes the deletion point, where the fluctuation of differences is much larger than that
of other points.
3.2. Framework of Inter-Frame Forgery Identification Scheme
In this paper, a CCCoGV based inter-frame forgery identification scheme is proposed. The framework of our
method is as shown in Figure 8.
Figure 4. Four continuous frames from a 100-frame-inserted video.
Figure 5. The CCCoGV of 100-frame-inserted video.
Figure 6. Three continuous frames from a 100-frame -deleted video.
0100200 300400
0
0.2
0.4
0.6
0.8
The Difference Between Correlation Coefficients

Q. Wang et al.
55
Figure 7. The CCCoGV of 100-frame-deleted video.
Original video
Original video
Video forgery
Video forgery
The differences of correlation
coefficients of gray values
The differences of correlation
coefficients of gray values
The differences of correlation
coefficients of gray values
The differences of correlation
coefficients of gray values
Normalization and
Quantization
Normalization and
Quantization
Normalization and
Quantization
Normalization and
Quantization
Statistical hist
distribution
Statistical hist
distribution
Statistical hist
distribution
Statistical hist
distribution
SVM
SVM
Figure 8. The framework of video inter-frame forgery identification.
The detailed scheme of our method is:
1) Calculate the differences between correlation coefficients gray values of adjacent frames.
This step is same to the previous work [5]. Equation (1-3) is used to calculate gray value Y, correlation coeffi-
cients 
 and difference of correlation coefficients
k
r∆
. For a video with M frames, get a
(1) 1M−×
vector to
store the differences of the video.
2) Normalize and quantize the vector.
For each vector of the array, find the maximum m and divide every element in the matrix by m. Then quantify
all the elements into D quantization levels. As all of them are distributed from 0 to 1, the quantization interval is
1/D.
3) Count the statistical distribution of differences.
After the normalization and quantization step, the
1 M−
elements are all discrete values from 1/D to 1 (1/D,
2/D,…,1). Count the distribution of the discrete values into a
1D×
vector. For a video database with N videos,
build an array contains N vectors of the statistical distribution to represent the CCCoGV features of all the vi-
deos in the database.
4) Distinguish original videos and video forgeries with Support Vector Machine (SVM) [6].
Put original video array and a video forgery array to the SVM, and distinguish them. Repeat 20 times and get
the average classification accuracy.
The difference between our method and previous works [5] is the purpose. Their method is used for detecting
where was tampered in a video. While our method is for the purpose to identify whether a video was tampered
and classify original videos and video forgeries.
As their method cannot distinguish the original and forgeries, we improve their method by using normaliza-
tion and quantization to obtain distinguishing feature of a fix length, and then use SVM to classify original vi-
deos and inter-frame forgeries.
4. Experimental Results
4.1. Experimental Object
In experiments, we use five video databases. One is original video database. The other four are tampered video
databases, of which each separately contains the videos inserted 25 frames, inserted 100 frames, deleted 25
frames and deleted 100 frames. There are 598 videos with still background and a little camera shake in each
video database (N = 598).
0100 200 300 400
0
0.1
0.2
0.3
0.4
0.5
The Difference Between Correlation Coefficients

Q. Wang et al.
56
Table 1. Classification accuracy.
Forgeries Experiment Video Databases(With original video database)
25-frame-insertion 100-frame-insertion 25-frame-deletion 100-frame-deletion
Accuracy 99.22% 99.34% 94.19% 97.27%
Table 2. Classifying accuracy of two kinds of forgeries.
Forgeries Experiment Video Databases
25-frame-insertion and 25-frame-deletion 100-frame-insertion and 100 -frame-deletion
Accuracy 96.21% 95.83%
4.2. Experimental Setting
In our experiments, we use SVM of polynomial kernel [6] as the classifier.
To train the SVM classifier, about 4/5 of the videos are randomly selected as the training set (480 originals
and 480 forgeries). The rest 1/5 form testing set (118 originals and 118 forgeries). The experiments are repeated
for 20 times to secure reliable classification results.
4.3. Results and Discussion
We extract CCCoGV features of the five video databases, and classify original video database and each of the
four tampered video databases separately. The accuracy was as shown in Table 1.
Obviously, this method is efficient to identify whether a video was tampered. As expected, the classifying
accuracy between originals and frame-inserted forgeries is higher than that of frame-deleted forgeries. But even
the classifying accuracy between originals and 25-f r a me -deleted forgeries is 94.19%, which is the lowest level
of the four experiments.
We then try to mix the four kinds of forgeries and classify them from originals. The result is efficient, too.
The accuracy is 96.75%.
Inspired by this, we further try to classify frame-inserted forgeries and frame-deleted forgeries. With the same
method, we classify the 25-fra me -inserted videos and 25-frame-deleted videos, as well as 100-frame -inserted
videos and 100-frame-deleted videos. The result is as shown in Table 2.
We can see that the accuracy still stays high. The method is effective in classifying two kinds of forgeries.
Then we mix the 25-frame-inserted videos and 100-frame-inserted videos, as well as 25-frame-deleted videos
and 100-frame-deleted videos. And classify the two kinds of mixed forgeries. The accuracy is 98.79%.
To sum up, our method is efficient in classifying original videos and forgeries as well as different kinds of
forgeries.
5. Conclusions
In this paper, we extract the consistency of correlation coefficients of gray values as distinguishing features to
classify original videos and forgeries. The classifying accuracies are high in all of the experiments indicate that
the proposed method is efficient in classifying original videos and forgeries yet different kinds of video forge-
ries.
In future work, we will find some new distinguishing features to identify the forgeries. And in view of the fact
that the videos we use in the experiments are under still background, we will build a new video database with
moved background.
References
[1] Hsu, C.C., Hung, T.Y., Lin, C.W. and Hsu, C.T. (2008) Video Forgery Detection Using Correlation of Noise Resi due .
2008 IEEE 10th Workshop on Multimedia Signal Processing.
[2] Li, L.D., Wang, X.W., Zhang, W., Yang, G.B. and Hu, G.Z. (2013) Detecting Removed Object from Video with Sta-

Q. Wang et al.
57
tionary Background. Digital Forensics and Watermarking. Lecture Notes in Computer Science, 7809, 242-252.
[3] Subramanyam, A.V. and Emmanuel, S. (2012) Video Forgery Detection Using HOG Features and Compression Prop-
erties. 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP), 89-94.
[4] Chao, J., Jiang, X.H. and Sun, T.F. (2013) A Novel Video Inter-Frame Forgery Model Detection Scheme Based on
Optical Flow Consistency. Digital Forensics and Watermarking, Springer, Berli n, Heidelberg, 267-281.
[5] Huang, TQ., Chen, Z.W., Su, L.C., Zheng, Z. and Yuan, X.J. (2011) Digital Video Forgeries Detection Based on Con-
tent Continuity. Journal of Nanjing University (Natural Science), 47, 493-503.
[6] Chang, C.-C. and Lin, C.-J. (2011) LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelli-
gent Systems and Technology (TIST), 27, 1-27. http://www.csie.ntu.edu.tw/~cjlin/libsvm/