Blind Video Watermarking Using Multilevel Hybrid ... · Blind Video Watermarking Using Multilevel Hybrid Regression Technique under Compression Based Attacks . Sakshi Batra . Chandigarh

Blind Video Watermarking Using Multilevel

Hybrid Regression Technique under Compression

Based Attacks

Sakshi Batra Chandigarh Group of Colleges, Mohali, India

Email: [email protected]

Rajneesh Talwar Chandigarh Group of Colleges, Jhanjeri, India

Email: [email protected]

Abstract—Whirlwind neoplasm of networks such as

Internet, wireless communication and Intranet has

facilitated the wide use of multimedia techniques and digital

data. Transmission of information has become rather swift

and sheer due to the same but as all good things come at a

price, this in turn has multiplied the casualties of

bushwhack and assail on the data that is being transmitted.

To fence this, many techniques have come into foray in the

recent past. These techniques are broadly categorized under

Copyright Protection Solutions. The main features of

information hiding are capacity, security and robustness.

Video watermarking usually prefers robustness. In a robust

algorithm to eliminate, the watermark without rigorous

degradation of cover content is not possible. This paper

introduce frequently used key techniques and Video

Watermarking with features required to robustly

watermark a video for a valuable application, algorithm is

based on Singular Value Decomposition (SVD) and

redundant wavelet transform. The algorithm showed high

level of imperceptibility, when compared with base

approach; performance varied with respect to robustness

and payload.

Index Terms—digital video watermarking, Singular Value

Decomposition (SVD), robustness, imperceptibility, DWT,

DCT

I. INTRODUCTION

Selling and Marketing of art works is easier using

multimedia techniques and internet but it also comes with

the hard fact that such procedure becomes vulnerable to

anti business activities like copying [1]. Thus Copyright

Protection is of utmost importance to facilitate

widespread and fool proof use of such advanced

technologies. Digital Watermarking is one such

technique that fire walls content owners from

mischievous elements [2]. Various types of information

is embedded in digital content using Digital

Watermarking. In simpler terms, a watermark is used as

information to validate the data and protect copyrights [3].

Manuscript received May 13, 2015; revised January 30, 2016.

Associated with the widespread circulation of videos are

issues of copyright infringement, authentication and

privacy. One possible solution is to embed some invisible

information into the videos where the embedded

information can be extracted for different purposes.

Digital watermarking is a process to embed some

information called watermark into different kinds of

media called Cover Work [4]. While some watermarks

are visible, most watermarks of interest are invisible.

There are many classes of invisible watermarks for

different applications such as fragile watermarks and

robust watermarks. Fragile watermarks are designed to be

broken easily by video processing operations. The broken

watermark serves as an indication of alteration of the

original video. Major applications include tampering

detection of videos placed on the WWW and

authentication of videos received from questionable

sources [5]. Robust watermarks are required to remain in

the watermarked video even after it has been attacked.

Digital Watermarking could also be considered as a

prolongation of Steganography, which has built its base

as an encouraging solution for copyright protection [6]. It

assesses superior control over embedded information.

The purpose of Digital Watermarking is that it does not

leave any conspicuous tag on the content which helps it

sustain its esteem. It is not possible to discard such

watermarking without demoting the content giving this

technique an upper hand over Cryptography. Such digital

watermarks are impalpable and could only be detected by

fit virtuoso.

The attacks may also be casual or unintended attacks

which are common video processing such as filtering,

compression, scaling, cropping, etc. Major applications

include ownership establishment, copyright and

distribution control. Data hiding watermarks, also called

steganography [7], are used to embed data in the videos

with the intention to have the data recovered perfectly at

the receiver. Such methods usually assume that there are

no hostile or even casual attacks. Data integrity is not

secure in image transfers. The copyright data may be in

the form of text [8].

International Journal of Electronics and Electrical Engineering Vol. 4, No. 6, December 2016

©2016 Int. J. Electron. Electr. Eng. 569doi: 10.18178/ijeee.4.6.569-574

mailto:[email protected]

mailto:[email protected]

Watermarking may be visible or invisible. Invisible

watermarking implies that the presence of the watermark

is barely discernible when the watermarked signal is

displayed. If the watermark cannot be easily removed

from the watermarked signal even after applying

common watermarking attacks then it is referred as

robust embedding. The basic components involved in

robust watermarking are watermark embedding, attack,

and watermark detection [9]. In watermark embedding, a

watermark signal (Text, image or audio etc.) is

constructed and then embedded into an original signal

(Video in context with this paper) to produce the

watermarked signal. Once embedding is done, the

watermarked video can be subjected to various attacks

[10]. During watermark detection, the watermark detector

is given a test signal that may be watermarked, attacked

or not. The watermark detector reports whether the

watermark is present or not on examining the signal at its

input [11].

Video watermarking can be classified categories based

on the method of embedding watermark information bits

in the video. The two categories are: Spatial

watermarking, and transform-domain watermarking. In

spatial watermarking, embedding and detection are done

on spatial pixel values or on the overall video data.

Spatial domain techniques are easier to implement,

however not robust against signal processing operations

like video compression. Transform domain algorithm,

alter spatial pixel values of the video according to pre-

determined transforms. Commonly used are the Fast

Fourier Transform (FFT), Discrete Cosine Transform

(DCT), the Singular Value Decomposition (SVD) and the

Discrete Wavelet Transform (DWT). The transform-

domain watermarking have proved to be more robust and

imperceptible when compared to spatial domain

transforms, as they disperse the watermark in a special

domain of video frame, proving it to be very difficult to

remove [12]. The DWT sections a picture into a lower

determination rough guess picture (LL) and also level

(HL), vertical (LH) and inclining (HH) subtle element

parts. The methodology can then be rehashed to process

numerous “scale” wavelet deterioration. One of the

numerous favourable circumstances over the wavelet

change is that that it is accepted to all the more precisely

model parts of the HVS as contrasted with the FFT or

DCT.

II. IMPORTANT ASPECTS OF VIDEO WATERMARKING

Video watermarking embeds data in the video for the

purpose of identification, annotation and copyright. A

number of video watermarking techniques have been

proposed. These techniques exploit different ways in

order to embed a robust watermark and to maintain

original video fidelity. Conventional encryption

algorithms permit only authorized users to access

encrypted digital data. Once such data are decrypted,

however, there is no way in prohibiting its illegal copying

and distribution [13].

Many algorithms for developing watermarks on

images are extended for videos. But some points need to

be considered during the extensions.

a) Between the frames there exists a huge amount of

intrinsically redundant data.

b) There must be a strong balance between the motion

and the motionless regions

c) Strong concern must be put forth on real time and

streaming video applications.

The following aspects are important for the design of

video watermarking systems.

a) Imperceptibility: The watermark embedding should

cause as little degradation to the host video as possible.

b) Robustness: The watermark must be robust to

common signal processing manipulations and attempts to

remove or impair the watermark.

c) Security: The embedded information must be secure

against tampering.

d) Capacity: The amount of embedded information

must be large enough to uniquely identify the owner of

the video.

Video watermarking is not a standalone technology. It

can be associated with different approaches to achieve a

sophisticated system. This research can be continuous by

applying this new proposed scheme to specific

environment or application and examine its usefulness.

III. SINGULAR VALUE DECOMPOSITION

Singular Value Decomposition (SVD) is a numerical

method for diagonalizing networks in which the changed

area comprises of premise expresses that is ideal in some

sense [8]. The SVD of a N×N network An is

characterized by the operation:

A=U S VT (1)

where U and V Є R N×N are unitary, and S Є R N×N is

an inclining grid. The diagonal entries of S are known as

the peculiar estimations of A and are thought to be

organized in diminishing request σi>σi+1. The sections

of the U grid are known as the left solitary vectors while

the segments of the V network are known as the right

independent vectors of A. Every peculiar worth σi points

out the luminance of a picture layer while the comparing

pair of independent vectors defines the geometry of the

picture [14]-[17].

IV. PROPOSED WORK

We propose a new innovative digital video

watermarking scheme which applies hybrid approach

using Singular Valued Decomposition and Redundant

Wavelet Transform in video. The proposed scheme is

robust against many watermark attacks, as the watermark

is embedded in the frames of video is strategically placed

with complex approach. To enhance the fidelity of the

scheme, key generation and wavelet based key

embedding watermarking scheme is presented. The new

watermarking scheme proposed is based on hybrid model

using singular values from watermark image after

resizing and using singular values of the wavelet


©2016 Int. J. Electron. Electr. Eng. 570

decomposed frame’s selected layer and also embedding

the watermark key obtained using key generation with

watermarks decomposed orthogonal values and

embedding in the 4-level decomposition of the selected

low energy band of the decomposed band. Experiments

have been performed on this video watermarking scheme

to prove its performance. We compare the results of the

proposed scheme with the existing base scheme with

different parameters and discuss the advantages and the

disadvantages of proposed scheme. The effectiveness of

this scheme is verified through a number of experiments.

A. Embedding Steps

1) Use of secret key, generation and embedding

Digital signature of the orthogonal matrices is a unique

binary string generated through a hashing function. In

addition, the digital signature must be random, so that an

attacker cannot predict them. Watermarking for tamper

detection leads to a similar situation as watermarking

videos. Being a secret key scheme, the same key is used

for both watermark insertion and extraction. Hence, the

key must be transmitted from the owner to the verifier

through a secure channel. Signature bits should be

embedded into high energy region for improved

robustness. Signature should remain robust against wide

range of attacks hence one set of signature bits are

embedded into LL4 and another set is embedded into HH4

band to ensure recovery from at least one of the band. The

algorithm for embedding and extracting the signature is

as follows.

2) Generation of secret code

(i) Add all elements of the column of orthogonal

matrices U and V obtained by SVD decomposition of the

prepared watermark and create 1×N array of values,

where N is the number of rows of the watermark logo.

(ii) Based on the median values of the U & V 1×N

dimension array, map the array values into corresponding

binary codes and obtaining array of the same size.

(iii) By XORing the binary array generates the code

for the given orthogonal matrices U & V.

3) Code embedding

(i) Generate the code of N bits for the U and V

matrices of watermark and the secret key by user and

then applying fuzzy generator to it.

(ii) Using Haar wavelet, decompose the selected layer

of the frame using the image into 4 sub-bands: LL, HL,

LH, and HH. Further decompose LL band to the 4th level.

(iii) Select N random coefficient from LL4 and HH4

band. Convert the integer part into the binary code of L

bits.

(iv) Replace the nth

bit of the coefficient with code bit

and then convert the binary code to its decimal

representation.

(v) Apply the IDWT with modified LL4 and HH4

band coefficients.

4) Watermark embedding algorithm

(i) Select the video to be watermarked and convert it

into frames of RGB24.

(ii) Convert the image frames to double scale.

(iii) Separate the layers of the image and select the

layer or layers to be watermarked.

(iv) Apply 2D wavelet and decompose image layer

into four sub-bands: LL, HL, LH, and HH on selected

layer of the image.

(v) Watermark W is decomposed using SVD and apply

SVD to Diagonal HH band and replace the values of

singular matrix.

W=Uw*Sw*VwT (2)

(vi) Select the other two layers and perform

watermarking on them by following steps VI to VII for

each layer.

(vii) Apply Redundant Wavelet Transform to the

Watermark image and then perform SVD to the HH band

filter image and replace the singular values with the

redundant singular values of the band to obtain second

watermark. Do the step for both blue and green layers.

Wr=Uw*Srw*VwT (3)

(viii) Repeat steps iv to vii for all the frames in the

video.

(ix) Convert all the watermarked frames to uint8

format and convert the frames into video format.

B. Extraction Steps

1) Code extraction

(i) Using Haar wavelet, decompose the selected layer

of the frame using the image into 4 sub-bands: LL, HL,

LH, and HH. Further decompose LL band to the 4th level.

(ii) Select N random coefficient from LL4 and HH4

band. Convert the integer part into the binary code of L

bits.

(iii) Extract the nth

bit from the coefficient to extract

the code.

(iv) Generate the code of N bits for the U and V

matrices of watermark and the secret key by user and

then applying fuzzy generator to it and compare it with

extracted code. If they match, authenticate U and V

matrices and use them in watermark estimation.

Decoder extracts the code and matches it with the

regenerated code for authentication of U & V matrices. If

matching criteria is satisfied, then decoder will continue

estimating watermark.

2) Watermark extraction algorithm

(i) Select the Watermarked Video and convert it into

frames of RGB24.

(ii) Convert the image frames to double scale. Separate

the layers of the image and select the layer or layers to be

watermarked.

(iii) If the Extracted signature is same as the generated

signature the normal extraction takes place.

(iv) Apply 2D wavelet and decompose image layer

into four sub-bands: LL, HL, LH, and HH on selected

layer of the image.

(v) Apply SVD to HH band.

HHw = Uh * Sh * Vht (4)

(vi) Extract the Singular values and recompose the

watermark using inverse SVD on it U and V matrix.

(vii) If the extracted signature does not match the

generated signature, we go for alternate extraction.



(viii) Selecting the blue and green layers of the image

and apply 2D wavelet and decompose image layer into

four sub-bands: LL, HL, LH, and HH on selected layer of

the image.

(ix) Using redundant wavelet, decompose the

watermark image into four and apply SVD to the HHr

filter image.

(x) Extract Singular values from the HH band obtained

using 2D-DWT in the layers decomposed and apply

inverse SVD to the HHr with the extracted value of

watermark.

HHw=Uw*Swe*Vwt (5)

(xi) Apply inverse redundant wavelet transform to

recompose the watermark with extracted values of the

watermark.

(xii) Compare the normalized co-relation with the

original watermark and plot the results.

(xiii) Compare the PSNR values and structural

similarity of the Original video to the watermarked video

and plot the results.

Fig. 1 and Fig. 2 show the block diagrams for

proposed Embedding and Extraction Process.

Figure 1. Proposed watermarking embedding process

Figure 2. Proposed watermarking extraction process

V. RESULTS

A. PSNR Values

Fig. 3 and Fig. 4 show the PSNR values for Base

scheme and Proposed scheme. Hence, this prompting a

PSNR degradation of around 29.79dB (28th

frame) for

Proposed Scheme and 24.07dB (28th

frame) for base

scheme.

Figure 3. PSNR values for base scheme

Figure 4. PSNR values for proposed scheme

B. Correlation Values

Fig. 5 and Fig. 6 show the Correlation values for Base

scheme and Proposed scheme. Hence, Correlation value

is 0.9179dB (28th

frame) for Proposed scheme and

0.9031dB (28th

frame) for Base scheme.

Figure 5. Correlation values for base scheme



Figure 6. Correlation values for proposed scheme

C. SSIM Values (Structure Similarity Index)

Fig. 7 and Fig. 8 show the SSIM values for Base

scheme and Proposed scheme. Hence, structural

similarity index increase of 0.9977dB (28th

frame) for

Proposed Scheme and 0.9561dB (28th

frame) for Base

scheme.

Figure 7. SSIM values for base scheme

Figure 8. SSIM values for proposed scheme

D. Extraction of Watermark without Any Attack

Fig. 9 shows retrieved Watermarks for Base Scheme

and Proposed Scheme. This is the normal outcome

without any Attack. Thus, visibility of watermark is more

using Proposed Scheme.

Figure 9. Retrieved watermark without any attack

E. Extraction of Watermark with Crop Attack

Fig. 10 shows retrieved watermark with crop attack

both for base scheme and proposed scheme. Hence,

watermark is more visible using Proposed Scheme and

the watermark is almost corrupt using Base scheme.

Figure 10.

F. Extraction of Watermark with Mean Attack

Fig. 11 shows the outcome with Mean Attack both for

Base Scheme and Proposed Scheme. The Visibility of the

watermark is far better using Proposed scheme.

Figure 11. Retrieved watermark with mean attack

G. Extraction of Watermark with Median Attack

Fig. 12 shows the outcome with Median Attack both

for Base Scheme and Proposed Scheme. The Retrieved

watermark is far better using Proposed Scheme and hence

it is more Robust.

Figure 12. Retrieved watermark with median attack

H. Extraction of Watermark with Noise Attack

Fig. 13 shows the outcome with Noise Attack both for

Base Scheme and Proposed Scheme. Thus, the



Retrieved watermark with crop attack

watermark is extracted successfully using Proposed

Scheme and watermark is corrupted using Base scheme.

Figure 13. Retrieved watermark with noise attack

I. Extraction of Watermark with Rotation Attack

Fig. 14 shows the outcome with Rotation Attack both

for Base Scheme and Proposed Scheme. Hence, Proposed

watermarking scheme is more robust as its watermark is

clearly visible with rotation attack.

Figure 14. Retrieved watermark with rotation attack

VI. CONCLUSION

The proposed scheme satisfies the requirement of

imperceptibility and robustness for a feasible

watermarking scheme. Moreover, A Blind process is

carried out so it does not need original data at the time of

Extraction and also it does not need any information in

the detection process while other algorithms and yet can

resist majority of the attacks in the process and no

authentication is given as a result tampering detection

cannot be detected. The watermark recovery is better at the

cost of perceptibility of the watermarked video in different

methods. The proposed scheme as discussed is robust

against attacks like Crop, Mean, Median, Noise and

Rotation attacks.

REFERENCES

[1] N. R. Bamane and S. B. Patil, “Comparison & performance analysis of different digital video watermarking techniques,”

International Journal of Scientific & Engineering Research, vol. 4,

no. 1, January 2013. [2] S. Batra and H. K. Khattra, “An improved data transfer technique

using steganography with watermarking and visual cryptography,”

International Journal of Innovative Technology and Exploring Engineering, vol. 3, no. 7, December 2013.

[3] P. V. Powar and S. S. Agrawal, “Design of digital video

watermarking scheme using matlab simulink,” IJRET, vol. 2, no. 5, May 2013.

[4] H. Shojanazeri, W. A. W. Adnan, and S. M. S. Ahmad, “Video

watermarking techniques for copyright protection and content authentication,” International Journal of Computer Information

Systems and Industrial Management Applications, vol. 5, pp. 652-

660, 2013. [5] M. A. Chimanna and S. R. Khot, “Digital video watermarking

techniques for secure multimedia creation and delivery,”

International Journal of Engineering Research and Applications, vol. 3, no. 2, March-April 2013.

[6] S. S. Bedi, R. Ahuja, and H. Agarwal, “Copyright protection using video watermarking based on wavelet transformation in

[7]

[8] the art, contributions and research challenges,” International Journal of Advanced Computer Science and Application, vol. 3,

no. 7, 2012.

[9] M. M. Sathik and S. S. Sujatha, “A novel DWT based invisible watermarking technique for digital images,” International Arab

Journal of e-Technology, vol. 2, no. 3, January 2012.

[10] S. Rungraungsilp, M. Ketcham, P. Surakote, and S. Vongpradhip, “Data hiding method for QR code based on watermark by

comparing DCT with DWT domain,” in Proc. International

Conference on Computer and Communication Technologies, May 26-27, 2012.

[11] J. Madia, K. Dave, V. Sampat, and P. Toprani, “Video

watermarking using dynamic frame selection technique,” in Proc. National Conference on Advancement of Technologies, 2012.

[12] B. Isac and V. Santhi, “A study on digital image and video

watermarking schemes using neural networks,” International Journal of Computer Applications, vol. 12, no. 9, January 2011.

[13] C. Cruz-Ramos, R. Reyes-Reyes, M. Nakano-Miyatake, and H.

Perez-Meana, “A blind video watermarking scheme robust to frame attacks combined with MPEG2 compression,” Journal of

Applied Research and Technology, vol. 8 no. 3, pp. 323-339,

December 2010. [14] L. Rajab, T. Al-Khatib, and A. Al-Haj, “Video watermarking

algorithms using the SVD transform,” European Journal of

Scientific Research, vol. 30, no. 3, pp. 389-401, 2009. [15] G. Bhatnagar and B. Raman, “A new robust reference

watermarking scheme based on DWT-SVD,” Computer Standards

& Interfaces, vol. 31, pp. 1002-1013, 2009. [16] H. H. Mirza, H. D. Thai, Y. Nagata, and Z. Nakao, “Digital video

watermarking based on principal component analysis,” in Proc. Second International Conference on Innovative Computing,

Informastion and Control, 2007.

[17] A. Al-Haj, “Combined DWT-DCT digital image watermarking,” Journal of Computer Science, vol. 3, no. 9, pp. 740-746, 2007.

Sakshi Batra is currently doing her M.Tech

Degree in Electronics and Communication in

Chandigarh Group of Colleges, Landran, Mohali (Punjab). She completed her B.E.

degree in Electronics and Communication from

Lovely Professional University, Jalandhar, India. Her area of interest includes Image

Processing, Networking and Network Security.

Dr. Rajneesh Talwar is presently working as

Principal of Chandigarh Group of Colleges-

COE, Jhanjeri, Punjab, India. He did his PhD in 2010 and M.Tech in 2002 from Thapar

University, Patiala, Punjab, India. He has

worked as Principal of CGC, Landran and Swift Technical Campus, Rajpura. He has been

Vice principal at RIMT Aggrasen Engineering

college, Mandi gobindgarh and Head, Electronics and communication engineering

department at RIMT-IET, Mandi gobindgarh, Punjab India.

Dr. Talwar has a U.S patent “Fiber Optic Point Temperature Sensor” to his credit, twenty + international Journal Publications, presented papers

/participated in more than twelve International conferences and many

national level conference participations. Invited Reviewer of 2014 IEEE Colloquium on Humanities, Science and Engineering Research

(CHUSER 2014), was Reviewer of MAEJO International Journal of

Engineering Science and Technology, Thailand and “Materials and Design”, a ELSEVIER International Journal.



multiband,” International Journal of Computer Applications, vol.

66, no. 8, March 2013.S. N. Ahmed, B. Sridhar, and C. Arun, “Robust video

watermarking based on discrete wavelet transform,” International

Journal of Computer Network and Security, vol. 4, no 1, Jan.-Mar.2012.

R. A. Sadek, “SVD based image processing applications: State of

Blind Video Watermarking Using Multilevel Hybrid ... · Blind Video Watermarking Using Multilevel Hybrid Regression Technique under Compression Based Attacks . Sakshi Batra . Chandigarh

Documents