Hybrid-Based Compressed Domain Video Fingerprinting Technique

Abstract

Video fingerprinting is a newer research area. It is also called “content-based video copy detection” or “content-based video identification” in literature. The goal is to locate videos with segments substantially identical to segments of a query video while tolerating common artifacts in video processing. Its value as a tool to curb piracy and legally monetize contents becomes more and more apparent in recent years with the wide spread of Internet videos through user generated content (UGC) sites like YouTube. Its practical applications to a certain extent overlap with those of digital watermarking, which requires adding artificial information into the contents. Fingerprints are compact content-based signature that summarizes a video signal or another media signal. Several video fingerprinting methods have been proposed for identifying video, in which fingerprints are extracted by analyzing video in both spatial and temporal dimension. However, these conventional methods have one resemblance, in which video decompression is still required for extracting the fingerprint from a compressed video. In practical, faster computational time can be achieved if fingerprint is extracted directly from the compressed domain. So far, too fewer methods are known to propose video fingerprinting in compressed domain. This paper presents a video fingerprinting technique that works directly in the compressed domain. Experimental results show that the proposed fingerprint is highly robust against most signal processing transformations.

Full text

Computer and Information Science; Vol. 5, No. 5; 2012
ISSN 1913-8989 E-ISSN 1913-8997
Published by Canadian Center of Science and Education
25
Hybrid-Based Compressed Domain Video
Fingerprinting Technique
Abbass S. Abbass1, Aliaa A. A. Youssif1 & Atef Z. Ghalwash1
1 Faculty of computers and information, Helwan University, Cairo, Egypt
Correspondence: Abbass S. Abbass, Faculty of computers and information, Helwan University, Cairo, Egypt.
E-mail: abbass1652@yahoo.com; aliaay@yahoo.com; aghalwash@edara.gov.eg
Received: June 4, 2012 Accepted: June 18, 2012 Online Published: July 15, 2012
doi:10.5539/cis.v5n5p25 URL: http://dx.doi.org/10.5539/cis.v5n5p25
Abstract
Video fingerprinting is a newer research area. It is also called “content-based video copy detection” or
“content-based video identification” in literature. The goal is to locate videos with segments substantially
identical to segments of a query video while tolerating common artifacts in video processing. Its value as a tool
to curb piracy and legally monetize contents becomes more and more apparent in recent years with the wide
spread of Internet videos through user generated content (UGC) sites like YouTube. Its practical applications to a
certain extent overlap with those of digital watermarking, which requires adding artificial information into the
contents. Fingerprints are compact content-based signature that summarizes a video signal or another media
signal. Several video fingerprinting methods have been proposed for identifying video, in which fingerprints are
extracted by analyzing video in both spatial and temporal dimension. However, these conventional methods have
one resemblance, in which video decompression is still required for extracting the fingerprint from a compressed
video. In practical, faster computational time can be achieved if fingerprint is extracted directly from the
compressed domain. So far, too fewer methods are known to propose video fingerprinting in compressed domain.
This paper presents a video fingerprinting technique that works directly in the compressed domain. Experimental
results show that the proposed fingerprint is highly robust against most signal processing transformations.
Keywords: video fingerprinting, compressed domain, perceptual hash
1. Introduction
Text, image, audio, and video can be represented as digital data. The explosion of Internet applications leads
people into the digital world, and communication via digital data becomes recurrent. However, new issues also
arise and have been explored, such as data security in digital communications, copyright protection of digitized
properties, and invisible communication via digital media (Nianhua, 2011). Due to the rapid development of
video production technology and the decreasing cost of video acquisition tools and storage, a vast amount of
video data is generated around the world every day, including feature films, television programs,
personal/home/family videos, surveillance videos, game videos, etc. Digital video has opened up the potential of
using video sources in ways other than the traditional serial playback. However, this requires the development of
new technologies for accessing and manipulating digital video (Moxley, 2010).
Additionally, the amount of digital video data, which has the potential of becoming much greater than that of
traditional analog video, necessitates the development of digital video management tools for handling massive
video databases. Also, the ease with which all digital media can be flawlessly copied makes the development of
appropriate rights protection and authentication tools highly desirable. There necessitates techniques for
automatically managing this vast amount of information, such that users can structure them quickly, understand
their content and organize them in an efficient manner. An emerging technology which is useful for the
management of video, particularly with respect to rights protection, is fingerprinting (Cherubini, 2009) and
(Peng, 2010) also known as perceptual hashing or replica detection. This is defined as the identification of a
video segment using a representation called fingerprint (or sometimes perceptual hash), which is extracted from
the video content (Saikia, 2011).
The fingerprint must uniquely identify a video segment, but does not necessarily need to represent its content.
Additionally, it must remain the same when a video segment is manipulated, usually by common video
processing operations such as resizing, cropping, histogram equalization, compression etc. Fingerprints can be

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used for establishing whether two given segments are either identical or derived from each other, and also for
establishing whether a video segment is identical with (or derived from) any segment within a given video
database (Liu, 2010).
Several video fingerprinting algorithms that work at the pixel level have been proposed. Working directly with
pixels is, nowadays, computationally feasible and accurate. However, those solutions do not address the
magnitude of the resources needed for such a system and little analysis is provided in order to use a video
fingerprinting solution in practical cases. The compressed domain processing techniques use the information
extracted directly from compressed bitstream, and therefore are more advantageous than the uncompressed
domain in computational means. To achieve this, the information already inherent in the video stream, which
was included during the compression stage, is utilized (Maria, 2009).
A partial decompression must still be done to extract the information necessary for processing, however this
overhead is small compared to full decompression of the video stream. It is shown that in MPEG video
decompression, approximately 40% of the CPU time is spent in Inverse Discrete Cosine Transform (IDCT)
calculations, even when using fast DCT algorithms (Young-min, 2000). This paper propose a video
fingerprinting technique that work directly in the compressed domain at the stage of variable length decoding
(Figure 1), so it is computationally more efficient than uncompressed domain techniques and even the methods
that utilize DCT coefficients that are partially decoded.

MPEG
Var ia bl eLength
Decoding
Inverse
Quantization IDCT
Post
Processing
Raw
Reference
Frames
Motion
Compensation
Parsing
Figure 1. Full decompression versus partial decompression of the compressed video
The bold segmented rectangle is the zone where the partially decoded-based techniques work and the rounded
rectangle is where the proposed technique work.
The paper is organized as follows: Related work is given in Section (2); Section (3) describes our proposed
fingerprinting technique; Experimental results, comparisons and discussion are shown in Section (4); Finally,
Section (5) concludes the paper and point out some directions for future work.
2. Related Works
Some video similarity detection methods use uncompressed MPEG video to directly extract the features. Content
of the frames, DC values of macroblocks or motion vectors are used as features. Ardizzone (1999) use motion
vectors for feature extraction. They use global motion feature or motion based segmented feature as a signature
of the video. In global motion extraction step, statistical distribution of directions (i.e., an angle histogram) is
calculated. The angle histogram is computed by dividing the [-180, 180] interval into subintervals. Sum of
magnitudes of motion vectors in intervals constructs the angle histogram. In motion based segmentation, motion
vectors are clustered and labeled. Labels are given according to the similarity of motion vectors or the histogram
of motion vector magnitudes. Dominant regions are taken into account in comparison step.
Joly, Frelicot and Buisson extract local fingerprints around interest points in (Joly, 2003). These interest points
are detected with the Harris detector and compared using the Nearest Neighbor method. They propose statistical
similarity search in (Joly & Buisson, 2005) and (Joly, 2005). Joly et al. use this method and propose
distortion-based probabilistic approximate similarity search technique in order to speed up scanning in content
based video retrieval framework (Joly, Buisson, & Frelicot, 2007). Zhao et al. extract PCA-SHIFT descriptors
and use it for video matching in (Zhao, 2007). They use the nearest neighbor search for matching and SVMs for
learning matching patterns with their duplicates. Law et al. propose a video indexing method using temporal
contextual information which is extracted from local descriptors of interest points in (Law-To, 2006) and

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27
(Law-To & Gouet-Branet, 2006). They use this contextual information in a voting function.
Poullot et al. present a method for monitoring a real time TV channel in (Poullot, 2007). They use the method for
comparing the incoming data with indexed videos in database. Innovations of the method are z-grid for building
indexes, uniformity-based sorting and adapted partitioning of the components. Lienhart et al. (Yang, 2004) use
color coherence vector to characterize the key frames of the video. Sanchez et al. (1999) discuss using color
histograms of key frames for copy detection. They test the developed system on TV commercials and the system
is sensitive to color variations. Hampapur (2000) uses edge features but he ignores the color variations. Indyk
(1999) use distance between two scenes as its signature. However, it is a weak and limited signature.
So far, too fewer methods are known to propose video fingerprinting in compressed domain, one of them which
DC coefficient is used to model the fingerprint (Mikhalev et al., 2008). Given the extracted DC coefficient, the
video fingerprint method constructs the video frame, the modeled video frame is evaluated to obtain the
key-frames of the video, which would be further analyzed for generating the fingerprints. Naphade (1999) use
histogram intersection of the YUV histograms of the DC sequence of the MPEG video. It is an efficient method
in terms of compression. Recently AlBaqir (2009) proposed a video fingerprinting method, in which motion
vectors are used to model the fingerprint. He considers utilizing motion vector to construct approximated motion
field since the motion vectors are commonly generated during video compression for exploiting the temporal
redundancy within a video.
3. Proposed Technique
In general, MPEG normally classify the video frames into I (intra) frame, P (predicted) frame and B
(bi-directional) frame, and each frame is divided to macroblocks (MB) which are 16x16 pixel motion
compensation units within a frame. I frames can only have Intra blocks. P and B pictures can have different
modes according to motion content. Macroblock type modes in P and B frames are given in Figure 2 and Figure
3 respectively. If intra coding is selected, the corresponding MB is encoded individually by exploiting the two
dimensional discrete cosine transform (2D-DCT) coefficients (Equation 1). On the other hand, if inter coding is
selected, the MB is then encoded using motion estimation-motion compensation (ME/MC) algorithms
(Richardson, 2003; Heath, 2002).


M1M1
2
12 i,jij
i0 j0
2n 1 j
x( n , n ) X c c cos 2M






 (1)
where,
and Xi,j is M×M block of pixels.
Figure 2. Macroblock type modes in P pictures

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The purpose of using ME/MC is to reduce redundancy in temporal direction. To exploit temporal redundancy,
MPEG algorithms compute an interframe difference called prediction error (Pei, 1999).
1
prediction error(i, j) MN
2
MN
m,n
22
mn m i,n j
[,t) ,t1)]
(I (I



 (2)
where M and N are macroblock sizes. For a given macroblock, the encoder first determines whether it is Motion
Compensated (MC) or Non Motion Compensated (NO_MC) and a scheme is used to determine whether the
current block is intra/inter coded based on the prediction error. The scheme can be quite complex, but the general
idea is to code the difference between target macroblock and reference macroblock when the prediction error is
small, otherwise, intra-code the macroblock. For normal scenes, prediction is performed in P and B frames.
When there is a scene change, this prediction drops significantly, which lead to some macroblocks to be encoded
in intra mode. Strictly speaking, if a frame is inside of a shot, then the macroblocks should be predicted well
from previous or next frames. However, when the frames are on the shot boundary, the frames cannot be
predicted from the related macroblocks, and a high prediction error occurs (Yeo, 1995). This causes most of the
macroblocks of the P frames to be intra coded instead of motion compensated.
Figure 3. Macroblock type modes in B pictures
The proposed technique fuses the macroblock type’s information and the motion field generated by using the
motion vectors in the MPEG stream to capture the intrinsic content of the video. Only VLC decoding and
number of macroblocks times addition operation is necessary to obtain the MB data (see the rounded rectangle
zone in Figure 1).
The proposed technique (Figure 4) is divided into two stages namely the fingerprint extraction stage and the
similarity matching stage respectively. In the first stage the compressed MPEG video clip is parsed to extract the
macroblocks information and motion vector data. Then, 10 bin one dimensional histogram is constructed using
the macroblocks types (Figure 5). The macroblocks types used to generate this histogram aggregate not only the
normal types in the normal scenes but also the types that happened in the scene change-like scenes. So tis
histogram carries important clues to the spatial and temporal content of the video. Lastly, the proposed technique
merge the before mentioned feature to the motion field feature (AlBaqir, 2009) to generate two-part fingerprint.
Let call the first part of the proposed fingerprint as MBTH (Macroblock Type Information Histogram) and the
second part MFH (Motion Field Histogram).

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Figure 4. Proposed technique block diagram

t
t
123 10
MBTH X , X , X ,...X (3)
10 t
i
i1
X



 (4)
where ηt is the total number of MB in frame t.
To further reduce the resultant fingerprint redundancy, the proposed technique quantizes the generated
fingerprint into a binary sequence (for each part separately as Figure 4 shows) using the median filtering. This
can be achieved by fixing a threshold level, and quantize every bin value within each histogram according to the
threshold. By having a fingerprint in a binary sequence, a more efficient matching process can be performed

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using Hamming distance between the compared fingerprints. Instead of using the histogram intersection
(Equation 5) or even the Jaccard coefficient (Equation 6) to compare the resultant fingerprints.
N
(i) (i )
i1
W ( Q, R ) min( Q , R )

 (5)
QR
J(Q,R ) QR

 (6)
where Q and R are two pair of fingerprints, each containing N bins.
Figure 5. The macroblocks types used in generating the first part of the proposed fingerprint
The fusion of the two-part fingerprint in the second stage is performed as follows:
12 1 2
(MFH ,MFH ) 1- ) (MBTH MBTHSimilarity Score ( , )


 (7)
Where α is the fusion parameter, Ψ is the distance metric, MFHx is the MFH for video x.
4. Experimental Results
To evaluate the performance of the proposed technique, a test set of 200 videos all taken from the ocean
(ReefVid) was used. Then attacks were individually mounted on the videos, so a new test set equals to 3600
videos was generated. The mounted attacks included added watermark, mosaic effect, Embossment effect,
flipping, blindness effect, cropping, contrast adjustment, brightness modification, and bit rate change as Table 1
illustrates. Also, the hardware spec of the PC used for the experiments was an Intel dual-core running at 2 GHz
with 3GB memory. However, all tests were ran using a single core. Finally, the technique used to compare with
is the motion field technique (AlBaqir, 2009) which also operates in the compressed domain as the proposed one
to be a fair comparison.

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Table 1. Distortions used in the study
Index Distortion
1 Watermark
2 Mosaic
3 Embossment
4 Horizontal Flipping
5 Vertical Flipping
6 Adding Horizontal Lines
7 Adding Vertical Lines
8 Cropping (Big Window)
9 Cropping (Small Window)
10 Contrast Adjustment(negative image)
11 Contrast Adjustment(maximum contrast)
12 Brightness Adjustment (-50%)
13 Brightness Adjustment (-25%)
14 Brightness Adjustment (+50%)
15 Brightness Adjustment (+25%)
16 Different Bit Rate(512 Kbps)
17 Different Bit Rate(800 Kbps)
Four sets of the experiments are conducted to study the following issues:
1) Determine the best value of the fusion parameter.
2) Studying the binarization issue.
3) Studying the behavior of the proposed technique against content-preserving attacks (Table 1) to investigate the
robustness and uniqueness of the proposed fingerprint.
4) Comparing the proposed work against existing technique working in the compressed domain (Motion Field
(AlBaqir, 2009)).
The average retrieval rate across all the 17-attacks for the proposed technique with and without using
binarization is shown in Figure 6. The figure shows that the proposed work is improved when using the
binarization, also using α=0.2 gives the best result in the two cases.

50
55
60
65
70
75
80
85
90
95
100
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Average Retrieval Ra te %
α
Proposed Technique Without Median
Proposed Technique With Median
Figure 6. The proposed technique performance using the median to binarize the proposed fingerprint

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Figure 7 depict the results of comparing the proposed technique against the base line technique (the motion field
(AlBaqir, 2009)) in detailed manner using the before mentioned distortions (from 1 to 17 as mentioned in Table
1). It is clear that the proposed technique outperform the motion field technique. Finally Figure 8 concludes that
the proposed fingerprint outperform its constituent fingerprints also the macroblocks types is more important
than the motion vectors. An acceptable reasoning to the results is as follows: the motion field technique try to
build the motion trajectories of the video content depending on using the available information in the
compressed domain, but not all the macroblocks in the compressed domain carry motion information rather a lot
of them are labeled as NO_MC or skipped macroblocks. On the other hand, the proposed technique alleviate this
drawback by using the information associated with each macroblock in the compressed stream (the macroblocks
types), and by the proper merging of the motion field and the macroblocks types.
0 102030405060708090100
Wa term a rk
Mosaic
Emb ossm en t
Horizontal Flipping
Vertic al Flip ping
Adding Horizontal Lines
Add ing Vertica l Lines
Cropping (Big Window(
Cropping (Sma ll Window(
Contrast Adjustment(negative image(
Con trast Adjustmen t(max imum con trast(
Brightness Adjustment (-50%(
Brightness Adjustment (-25%(
Brightness Adjustment (+50%(
Brightness Adjustment (+25%(
Different Bit Ra te(512Kbps(
Different Bit Ra te(800Kbps(
Ave rag e Retr iev al Ra te %
Distortion
Proposed Technique Motion Field Technique
Figure 7. The performance of the proposed technique against the baseline technique across all used distortions

74.11%
82.35%
93.52%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Motion Field Feature Macroblocks Type Feature Proposed
Avera g e Retrieva l Rate %
Feature
Figure 8. Comparison of the different fingerprint extraction methods

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5. Conclusion
This paper proposes a video fingerprinting method in the compressed domain that utilizes the macroblock and
the motion vectors information in a hybrid way. The proposed work gives promising results despite of its low
computational overhead against a large spectrum of the content-based video transformations. Also, the proposed
work shows that the macroblocks types is more important than the motion vectors as intrinsic content-preserving
feature in the video compressed domain. One direction for future work is combining this technique with
compressed domain watermarking methods to design a robust content management methodology and apply it in
the broadcast monitoring area. Also, the proposed work can be adapted to work in real time environment like
cellular phones associated with cloud computing metaphor.
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