In recent years due to advancement in video and image editing tools it has become increasingly easy to modify the multimedia content. The doctored videos are very difficult to identify through visual examination as artifacts left behind by processing steps are subtle and cannot be easily captured visually. Therefore, the integrity of digital videos can no longer be taken for granted and these are not readily acceptable as a proof-of-evidence in court-of-law. Hence, identifying the authenticity of videos has become an important field of information security. In this thesis work, we present a novel approach to detect and temporally localize video inpainting forgery based on optical flow consistency. The proposed algorithm comprises of two stages. In the first step, we detect if the given video is inpainted or authentic and in the second step we perform temporal localization. Towards this, we first compute the optical flow between frames. Further, we analyze the goodness of fit of chi-square values obtained from optical flow histograms using a Guassian mixture model. A threshold is then applied to classify between authentic and inpainted videos. In the next step, we extract Transition Probability Matrices (TPMs) by modelling the optical flow as first order Markov process. SVM based classification is then applied on the obtained TPM features to decide whether a block of non-overlapping frames is authentic or inpainted thus obtaining temporal localization. In order to evaluate the robustness of the proposed algorithm, we perform the experiments against two popular and efficient inpainting techniques. We test our algorithm on public datasets like PETS and SULFA. The results show that the approach is effective against the inpainting techniques. In addition, it detects and localizes the inpainted frames in a video with high accuracy and low false positives.

1. Video Inpainting detection using inconsistencies in
Optical Flow
Thesis Committee
Dr. A V Subramanyam (Advisor)
Dr. Pradeep Atrey(External Reviewer)
Dr. Sambuddho Chakravarty(Internal Reviewer)
Shobhita Saxena
M.Tech CSE (MT13015)
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2. Outline
 Research Motivation and Aim
 Related Work and Research Contribution
 Problem definition
 Proposed Algorithm
 Experimental Results and Comparison Analysis
 Limitations
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3. Outline
 Research Motivation and Aim
 Related Work and Research Contribution
 Problem definition
 Proposed Algorithm
 Experimental Results and Comparison Analysis
 Limitations
3

4. Aims at :
 restoring lost parts of videos and
reconstructing them based on the
background information and
spatial - temporal details.
 removal or replacement of
unwanted objects from frames
such that no distortion is observed
when video is played as a
sequence.
What is Video Inpainting?
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Figure 1 : Removal of unwanted object from
a video frame

5. Video Inpainting as Video Forgery
 Video Inpainting can be used as a major forgery tool to perform malicious changes in
videos such as object removal .
5Figure 2 : Video Inpainting as forgery type (a) source video frames (b) Inpainted video frames

6. Research Motivation
 Video Inpainting forgery produces inpainted regions in video frames in a
visually plausible manner.
 Inpainting forgery is highly sophisticated and gets more difficult to detect
in comparison to other forgery types thus posing a challenging research
problem.
 Very few works have been proposed in the area of video inpainting
detection.
 Existing inpainting detection techniques fail to perform detection of latest
state-of-the-art inpainting techniques.
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7. Research Aim
 To study and analyse the optical flow pattern in source and inpainted
videos .
 To present a robust technique for effective detection and localization of
inpainted regions in a given video sequence.
 To propose a single algorithm which detects multiple inpainting techniques.
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8. Outline
 Research Motivation and Aim
 Related Work and Research Contribution
 Problem definition
 Proposed Algorithm
 Experimental Results and Comparison Analysis
 Limitations
8

9. Related Work
Hsu et al. [1] Proposed approach to locate forged regions in an inpainted video
using correlation of noise residue.
Zhang et al. [2] Performs inpainting forgery detection based on ghost shadow
artifacts.
Das et al.[3] Proposed a blind detection method based on zero-connectivity feature
and fuzzy membership function to detect video inpainting forgery.
Lin et al. [4] Proposed spatio-temporal coherance based approach for video
inpainting detection and localization.
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10. Research Contribution
 Propose an approach which investigates video inpainting forgery which has not
been studied much in previous works
 Propose a novel approach to detect and temporally localize inpainted regions in a
given video sequence.
 Propose algorithm performs well in detection and localization of popular and
effective state-of-the-art inpainting techniques on which other inpainting detection
algorithms fail to perform .
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11. Outline
 Research Motivation and Aim
 Related Work and Research Contribution
 Problem definition
 Proposed Algorithm
 Experimental Results and Comparison Analysis
 Limitations
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12. Problem definition
 Given an input video sequence VN having N frames, determine if its an authentic
or an inpainted video .
 For a video that gets classified as inpainted, perform temporal localization of the
inpainted regions.
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13. Outline
 Research Motivation and Aim
 Related Work and Research Contribution
 Problem definition
 Proposed Algorithm
 Experimental Results and Comparison Analysis
 Limitations
13

14. Targeted Inpainting Techniques
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 This algorithm performs on two variants of Temporal Copy Paste (TCP) inpainting.
They are as follows :
 Conventional TCP – one of the first and most popular inpainting techniques
proposed by Patwardhan et al [5] in 2007. Handles relatively simple motion
types in videos for inpainting.
 Complex TCP – one of the state-of-the-art inpainting techniques proposed by
Alasdair et al [6] in 2014. Handles relatively complex motions in videos to
performing inpainting.

15. Algorithm Design
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16. Optical Flow
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 Optical Flow (Of) characterizes
the motion of every pixel in one
image to its corresponding
location in next image[7].
 Performs motion estimation in
between video frames .
 It is best applied to video frames
as these are sequence of time
ordered images.
Figure 3 : (a,b) mouth regions of two consecutive
images of a person speaking . (c) Flow field
estimated using optical flow.

17. 17* Picture from Khurram Hassan-Shafique CAP5415 Computer Vision 2003
Figure 4 : Two frames of a video sequence

18. 18
Figure 6 : Colour scheme used to
represent the orientation and
magnitude of optical flow
Figure 5 : Optical Flow computation in between two
images

19. Optical Flow Computation - Procedure
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V video under consideration having N
frames
m number of optical flow matrices
computed for each frame
N-m number of frames chosen for optical
flow computation
Of Optical flow matrix computed in
between frames as Of(n,n+1), Of(n,n+2),
Of(n,n+3)…. Of(n,n+m) , where n is a
particular frame
(N-m)*m Number of optical flow matrices
generated for each frame in a video
sequence

20. Optical Flow – Results
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a) Source video frames
b) Optical Flow of source framesFigure 7:

21. 21
a) Inpainted video frames
b) Optical Flow of inpainted video framesFigure 8:

22. Chi-Square Distance computation - Procedure
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H Histogram vector
computed for each
optical flow matrix..
chisq Chi – square distance
computed for
comparing histograms
(N-m)*(m-1) Number of chi –
square values
produced

23. Guassian Mixture curve fitting
 Applied to normalized chi – square values
 Measures goodness-of-fit for authentic and inpainted videos by giving
Root Mean Square Error(RMSE) values
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24. GM Distribution Fit to chi-square values
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Figure 9 : Source and Inpainted GM distributions

25. Markov Chain – feature extraction
 Each video VN divided into VN / k frames set
 Optical Flow Of of each frame set is modelled as firsst order spatial
Markov Chain
 Values in Of are rounded of to nearest integer values to get integer
value states and then truncated in between –Tr to +Tr before
extracting the transition probabilities .
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26.  Number of states to model a markov chain = (2Tr+1)
 For each matrix, number of Transition Probabilities = (2Tr+1) * (2Tr+1)
 TPM is constructed as :
where, u,v ϵ [-Tr , Tr] , and u,v ϵ Z.
Similarly, probabilities can be estimated for other directions.
 Perform SVM classification on above obtained TPMs.
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27. Outline
 Research Motivation and Aim
 Related Work and Research Contribution
 Problem definition
 Proposed Algorithm
 Experimental Results and Comparison Analysis
 Limitations
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28. Dataset
 Experiments have been conducted on test videos of two inpainting
techniques - complex TCP and convenional TCP .
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Table 1 : Conventional TCP inpainting dataset

29. 29
Table 2 : Complex TCP Inpainting Dataset

30.  RMS value threshold τ is empericaly set to 3.5.
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Figure 10 : RMS value based classification for complex TCP inpainting

31. 31
Figure 11 : RMS value based classification for conventional TCP inpainting

32. Performance Evaluation
 Performance has been measured by Precision, Recall and Accuracy .
 Precision(P) = TP/(TP+FP)
 Recall (R) = TP/(TP+FN)
 Accuracy(A) = (TP+TN)/(TP+TN+FP+FN)
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TP True Positive
TN True Negative
FN False Negative
FP False Positive

33. Results - Video Inpainting Detection
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Table 3:Classification Results for Complex TCP Table 4 : Classification Results for Conventional TCP
Table 5 : Video Inpainting Detection Results

34. Results - Video Inpainting Localization
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Table 6 :Classification Results for Complex TCP Table 7 : Classification Results for Conventional TCP
Table 8 : Video Inpainting Localization Results

35. Comparison
 Proposed approach is compared with spatio-temporal coherence
based technique proposed by Lin et al [4]for inpainting detection and
localization .
 Spatio- Temporal coherence based approach fails to perform on
complex TCP inpainting dataset.
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36. 36
Figure 12: Spatio_Temporal Approach Result on conventional TCP dataset

37. 37
Figure 13 : Spatio_Temporal Approach Result on complex TCP dataset

38. Outline
 Research Motivation and Aim
 Related Work and Research Contribution
 Problem definition
 Proposed Algorithm
 Experimental Results and Comparison Analysis
 Limitations
38

39. Limitations
 Considers only static camera videos with no camera motion.
 Multiple objects removal case not considered in complex inpainting dataset .
 Spatial Localization is not performed of inpainted regions is not performed.
 Dataset is small .
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40. References
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1) Chih-Chung Hsu, Tzu-Yi Hung, Chia-Wen Lin, and Chiou-Ting Hsu. Video forgery detection
using correlation of noise residue. In Multimedia Signal Processing, 2008 IEEE 10th
Workshop on, pages 170– 174. IEEE, 2008.
2) Jing Zhang, Yuting Su, and Mingyu Zhang. Exposing digital video forgery by ghost
shadow artifact. In Proceedings of the First ACM workshop on Multimedia in
forensics, pages 49–54. ACM, 2009
3) Sreelekshmi Das Gopu Darsan and Shreyas L Divya Devan. Blind detection method
for video inpainting forgery
4) Cheng-Shian Lin and Jyh-Jong Tsay. A passive approach for effective detection and
localization of region-level video forgery with spatio-temporal coherence analysis.
Digital Investigation, 11(2):120– 140, 2014

41. References
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5) Kedar Patwardhan, Guillermo Sapiro, Marcelo Bertalimo, et al. Video inpainting
under constrained camera motion. Image Processing, IEEE Transactions on, 16(2):545–
553, 2007.
6) Alasdair Newson, Andres Almansa, Matthieu Fradet, Yann ´ Gousseau, and Patrick
Perez ´ . Video inpainting of complex scenes. 2015
7) Thomas Brox, Andres Bruhn, Nils Papenberg, and Joachim We- ´ ickert. High
accuracy optical flow estimation based on a theory for warping. In Computer Vision-
ECCV 2004, pages 25–36. Springer, 2004