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Key Frames Based Video Authentication Using Fragile Watermarking and Singular Value Decomposition Assma Azeroual and Karim Afdel Abstract –It is becoming easier to alter a digital content in ways that are difficult to detect. If this digital content was a medical video or a critical video piece of evidence in a legal case, this form of modification might pose serious problems. In order to deal with this issue, this paper presents a fragile watermarking scheme to authenticate uncompressed video using key frames, singular value decomposition and Faber Schauder discrete wavelet transform. The video key frames are extracted where each key frame presents a video sequence and comes in the end of it. Then the Faber Schauder dominant blocks singular values of every frame are computed and placed in a matrix to be used later as a mark. This mark is inserted in the key frame salient regions. These operations are repeated for every sequence separately. Using this fragile watermarking scheme we show that any frame alteration can be detected and the video quality remains high. The method is fast and fragile to any modification. Keywords: Video Authentication, Video watermarking, Dominant Blocks, SVD, Video Tamper Detection, Key frames, FSDWT.

Nomenclature

f ijk

: Matrix : Number of matrix lines : Number of matrix columns : m  m orthogonal matrix : n  n orthogonal matrix : Transposed matrix of U : Singular values : Vector of m elements : Vector of n elements : Original image : Number of scales : The value of pixel  i, j  at scale number k

g ijk

: A linear combination of neighboring coefficient

M

m n U V UT i ui vi f0

N

I.

Introduction

Owing to the high growth rates of technologies, especially in multimedia contents, the tampering, modification, and distribution of digital content have become easy. The authentication of digital content is required and more techniques deciding the importance of integrity are found. A number of methods exist to verify and authenticate a digital content (image, audio and video). However, very few methods exist to deal with uncompressed video authentication. Some techniques add a

Assma Azeroual , Karim Afdel

cryptographic signature to the digital content, others used forensic analysis and some others have recourse to watermarking in order to authenticate a digital content. There are two important benefits in using watermarks for content authentication. The first one is that watermarks remove any need to store separated or associated metadata, such as cryptographic signatures. This can be substantial in systems that must deal with legacy issues, such as old file formats that lack fields for the necessary metadata. The second advantage of watermarks is that the watermark undergoes the same transformations as the work in which it is embedded. Contrary to an appended signature, the watermark itself changes when the work is corrupted. Many video authentication watermarking techniques have been proposed Recently. In [1], an algorithm for video MPEG-2 authentication was proposed, the features of I-frame are extracted by compressed sensing algorithm and embedded in the I-frame low-frequency discrete cosinus transform coefficients. Yanjiao Shi et al. [2] proposed an algorithm of watermarking for video authentication based on object dual, where the watermark is generated from each frame index and inserted into the moving objects of the corresponding frame, in order to detect the temporal tampering. As an other watermark, the principal content and the moving objects details combined with the authentication code are inserted into the frame aiming to detect the spatial tampering location and recovery. Later, Fallahpour et al. [3] proposed a method to detect tampering in compressed digital video using watermarking, the watermark is generated from the macroblock’s and frame’s indices, then it is inserted into the nonzero quantized DCT value of blocks. In [4], a fragile video watermarking scheme for tampering detection and localization was proposed, two watermarks are generated, the first one is bits of the digital signature of the frame hash value in frequency domain, the second one is constructed by micro-blocks numbers and frame numbers bits. Both watermarks are then embedded into video frames in highest non-zero quantized DCT coefficients. Unfortunately, a number of these methods are focused on authenticating compressed videos and there are a few methods that deal with uncompressed video authentication. Furthermore, several schemes embed the watermark in all video frames or in P-frames which requires an important time of execution, thus, due to the huge number of images that compose a video these methods are not suitable for applications that require low complexity algorithms such as video streaming and realtime applications. This paper proposes an uncompressed video fragile watermarking scheme using singular value decomposition (SVD), key frames and Faber Schauder discrete wavelet transform (FSDWT). SVD is often used in digital content authentication especially for images. In our method, SVD is used to extract video key frames and to generate a very fragile watermark. The FSDWT is used to select relevant regions where the watermark will be embedded, unlike the majority of fragile watermarking methods that insert arbitrarily the watermark in the image. The relevant selected regions in our technique are the frame contours and the neighboring textured regions. Thus, any modification of these regions will result in a modification of dominant blocks, hence the dominant blocks singular values will be modified also. Since the video key frames carry the most important information in the video, it is relevant to insert the watermark in it. The video is divided into a number of sequences, every sequence is delimited by two key frames. For every sequence, the dominant blocks singular values of each frame are computed, assembled in a matrix and XORed with a mark. After that, the singular values of key frame dominant blocks are computed and transformed into 16 bits representation, these bits are XORed with the bits obtained previously and used as a watermark. This later is embedded in the contours and the neighboring textures of the key frame that comes immediately after the sequence. These operations are repeated for each sequence to finally watermark the whole video. The proposed method has a low time of execution due to the strategy used to insert the watermark and the computation of dominant blocks using FSDWT which contains simple


arithmetic operations. Furthermore, this scheme is difficult to forge since the watermark embedded is calculated from the frame that will be watermarked, this is because an adversary can easily forge a fragile watermark if the inserted pattern is independent of the cover work [5]. In addition, the algorithm is very fragile to modifications, any frame alteration will result in a modification of the frame’s singular values, hence, the mark inserted in the key frame will be changed also, then we can infer that the video is not authentic. The rest of the paper is organized as follows. SVD and FSDWT are described in section II. The details of video fragile watermarking proposed method are described in section III and the experimental results are presented in section IV. The last section V draw the paper conclusions. II.

Background and Theory II.1. SVD

A number of image processing applications used SVD for different purposes as compression, hiding information and noise reduction. That's due to the properties tat SVD has in representing the image energy, hence, the image can be approached by the few first singular values [7]. According to [6], for a real matrix M m,n , it exist a real orthogonal matrices U  u1 ,u2 ,...,um  and V  v1 ,v2 ,...,vm  that satisfy :



U T MV  diag 1 , 2 ,..., p



1

with p  min  m,n  and 1   2     p ,  i , i  1, , p are called singular values of the matrix M . II.2. FABER SCHAUDER DISCRETE WAVELET TRANSFORM There are many benefices in using FSDWT for image processing. The first benefice is that FSDWT contains just arithmetic operations owing to the use of lifting scheme [9], thus, the FSDWT has a low complexity and it is very simple. The following algorithm describes the lifting scheme:  f 0  fij for i, j  Z   for 1  k  N   0 k 1  fij  f  k k1 k2 k3  2  gij  gij , gij , gij   gijk1  f 2ki 11,2 j  12 f 2ki,21j  f 2ki 12 ,2 j   g k 2  f k 1  1 f k 1  f k 1 2i,2 j 1 2i,2 j 2i  2 ,2 j  2 2  ij  k3 k 1 k 1 k 1 k 1 k 1 1  gij  f 2i 1,2 j 1  4 f 2i,2 j  f 2i,2 j  2  f 2i  2 ,2 j  f 2i  2 ,2 j  2



 











The second benefice of using FSDWT is that the contours and textured regions of an image are detected in an efficient manner using the FSDWT. This transform redistributes the image contained information which is carried by the extrema coefficients of wavelet [8]-called dominant coefficients. These later are localized on the image contours and textured regions. In order to facilitate the selection of these dominant coefficients in all sub-bands, we use the mixed scales representation that put each coefficient at the point where its relative functions reach their maximum. Fig. 2 shows the mixed scales representation of the image shown in Fig. 1. The figure Fig. 2 represent a coherent image obtained visually with textured regions and contours, these regions contain a high density of dominant coefficients [10][11].


Fig. 1. Original image

Fig. 2. Mixed scales representation. The lifting Scheme of the FSDWT is given by the following algorithm :

III. Proposed Watermarking Scheme We have proposed in our previous work [13] a method for image fragile watermarking based on SVD and FSDWT, the watermark was generated from the dominant blocs singular values of the image which were embedded in the LSBs of its dominant blocks in the frequency domain, this method has a low complexity for image authentication and can be applied to all frames of a video to authenticate it. But this method will take a long time especially for the videos that have a huge number of frames. To solve these problems, we propose in this paper a different fragile watermarking scheme to authenticate a video by embedding the watermark only in key frames. This watermark is generated from the features of each video sequence frames XORed with the bits obtained from the singular values of key frames dominant blocks. III.1. WATERMARK EMBEDDING PROCEDURE In the watermarking procedure, for each key frame, the LSB planes of dominant blocks pixels are replaced with the watermark data. This later is obtained by combining three matrices. The first


matrix is a mark, the second one is formed by dominant blocks singular values of a sequence frames and the third one is the dominant blocks singular values of the key frame to be marked. Since they are sensitive to any modification [14], singular values authenticating data are used. Any change in the frame will cause a change in the singular values of the dominant blocks of the key frame in which the watermark is embedded.This will have a direct impact on the contours and the neighboring textured regions which uniquely characterize an image. In addition, the FSDWT gives us good results in the term of perceptibility and has a low complexity. III.2. DOMINANT BLOCKS

In [12][16], the standard deviation  1 of FSDWT coefficients in mixed scales representation and the local deviation  2 were used with each 8  8 block as a rule to detect dominant blocs. Given a certain threshold  chose by the user, if  2  1 the block is considered as a dominant block. To obtain more precision and to fix automatically the threshold  , we use the Otsu threshold [15] with blocks of 4  4 dimension, the steps below are done to achieve this goal:  Apply the FSDWT on the image  Divide the obtained matrix into blocs of 4*4 dimension  Compute the local deviation for every bloc  Compute the Otsu threshold of the transformed image  For each bloc, compare sigma with lamda, if the bloc is dominant. The figure Fig. 3 compare our method to the method used in [12]. Original images are presented in the first column, the positions of pixels that correspond to the dominant blocks using the method in [12] are shown in the images at the second column while the third column presents the same images as in the second column using our method based on Otsu threshold. .


Fig. 3. Comparison between the method in [12] and our method to detect dominant blocks. First column: Original images. Second column: Dominant blocks obtained using the method in [12]. Third column: Dominant blocks obtained using our method. III.3. KEY FRAMES EXTRACTION A video is composed of successive images (frames), the difference between two consecutive frames is not significant. Hence, one frame is sufficient to present a shot or a sequence of consecutive frames, this frame is called key frame. The important information on a video is contained by key frames[18]. In [17], a key frame extraction method using FSDWT and SVD was proposed. For each frame the dominant blocks are computed, after that, feature vectors are extracted from the dominant blocks image of every frame and arranged in a feature matrix. SVD is then used to compute the ranks of these matrices sliding windows. Key frames are extracted by tracing the obtained ranks.


Fig. 4. The algorithm of key frame extraction.

III.4. WATERMARK CONSTRUCTION Let S1 ,S2 ,...,Si ,...,Sq be the sequences of the video and F i1 , F i 2 ,..., F i j ,..., F i pi the frames of a sequence Si and pi the number of frames of this sequence. The generation of watermark is done in two steps: The first step is to compute the matrix of dominant blocks singular values of a sequence frames. The video is composed by several sequences separated in our case by key frames. Let F i j be a frame in a sequence Si and K i the key frame coming immediately after this sequence. Firstly, We compute the dominant blocks of F i j and put them in a matrix DB j . Secondly, we compute the singular values of this matrix. Finally, we keep the first singular value SV j and we put it in a matrix M i . The same operations are repeated for every frame in the sequence Si , then we code in r bits

every element of the matrix M i . After that we XOR them with a secret vector, we obtain in the end a mark composed of a set of bits Vi . The figure Fig.5 shows the first step operations.


Fig. 5. The first step to generate the watermark. The second step is to compute the dominant blocks singular values of the key frame in which the watermark will be embedded. This step is done by the following: 1) Set LSB plan of the gray scale key frame to 0. 2) Compute the dominant blocks of the image obtained in the previous step. 3) Compute singular values of each dominant block and convert the first three singular values to 16 bits representation. 4) Apply XOR operation between the bits of Vi and the bits obtained in step 3. The watermark obtained is then inserted into the LSBs plan of the gray scale key frame dominant blocks positions. The same operations are done to embed the watermark in all video key frames and finally obtain the watermarked video. The Fig. 6 illustrate the video watermarking algorithm.


Fig. 6. The schematic diagram of watermarking embedding. III.5. WATERMARK VERIFICATION We repeat the same steps as in the Watermark construction for the watermarked video to obtain a mark composed by a set of bits Vi* . Then, the watermark verification for each key frame is done by the following: 1) 2) 3) 4)

Extract bits from LSBs plan of the gray scale key frame dominant blocks pixels. Set LSB’s plan of the key frame to 0. Compute the dominant blocks of the image obtained in the previous step. Compute singular values of each dominant block and convert the first three singular values to 16 bits representation. 5) Apply XOR operation between the bits of Vi* and the bits obtained in step 4 T . 6) Compare the bits obtained in step 1  S  with the bits obtained in step 5  S*  , if S = S * the keyframe is not authentic, hence the video has been modified. To locate the tampered frame we do the following for each video sequence: 1) XOR the bits S with the bits T, then XOR the obtained bits with the secret vector.Compute the dominant blocks of the image obtained in the previous step. 2) Divide the obtained bits into groups of r bits, convert them to numbers and put them in a matrix Ni* 3) Compare element by element the matrix Ni* with the matrix of singular values M i* calculated while computing the vector Vi* . 4) The rank of the elements which are not equal is the index of the tampered frame.

IV.

Experimental Results

In order to evaluate the performances of the proposed


video authentication approach, we used two datasets containing various numbers and types of videos. The first dataset holding five videos was downloaded from the Open Video project (www.openvideo.org). The second dataset holding more challenging videos was download from www.see.xidian.edu.cn/vipsl/database_Video.html) The table 1 shows these videos characteristics and table 2 shows the time required in the process of watermark embedding and verification. The visual quality of the watermarked video is used as the performance measure. The PSNR (Peak signal to noise ratio) of the watermarked video is used as the visual quality metric. All the video frames do not change after the watermarking embedding except the key frames, thus, we compute just the PSNR of the key frames. Table 2 shows also the average PSNR of each watermarked video key frames. The quality of the watermarked key frames is very high. As a consequence, the difference between the original key frames and the watermarked key frames is not noticeable Fig. 6. Note that, the proposed approach has been fully implemented and optimized in OpenCV C++ using a system of an Intel core i7 CPU and 8 GB in the memory. PSNR is measured using the formula: PSNR  10  log10

2552 MSE

 3

Where MSE is the quadratic mean error between the original image I and the watermarked image WI.Images are of size h  w . 1 h w MSE    I i, j   WI i, j  h  w i 1 j 1

2

 4

TABLE 1 EXPEROMENTAL RESULTS- PART 1 Video ID

1

2

Video Drift Ice as a Geologic Agent, seg5 Hurricane ForceAcoastal Perspective seg3

#frames

#key frames

2185

12

2308

19

3

Exotic Terrane, seg4

4795

26

4

Introduction to HCIL 2000 rep

2439

19

5

The Future of Energy Gases, seg5

3613

15


Video ID

TABLE 2 EXPEROMENTAL RESULTS- PART 2 Embedding Verification time(s) time (s)

Average PSNR

1

30.4

29.01

51.13

2

45.8

42.31

51.20

3

65.4

57.27

51.18

4

42.8

40.18

51.44

5

57.6

51.45

51.41

Fig. 6. Comparison between some watermarked key frames in the second column and their original key frames in the first column.

V.

Conclusion

The paper described key frames based fragile watermarking algorithm for uncompressed video authentication using SVD and FSDWT. The fragility of the proposed algorithm becomes higher by generating the watermark from the singular values of each sequence frames dominant blocks which give the algorithm more fragility. In addition, the algorithm had a better security after XORing them with a secret set of bits. The watermark was embedded in the key frame dominant blocks positions


which coincided with the contours and the neighboring textured regions, hence we obtained a good result in terms of perceptibility. The algorithm gave good results in terms of complexity and can be used in applications requiring low complexity. More work should be done to detect the tamper regions in a frame altered in the case of attacks.

Acknowledgements This work was supported by the Centre National pour la Recherche Scientifique et Technique (CNRST), funded by Moroccan government. References [1] C. Xiaoling and Z. Huimin, A Novel Video Content Authentication Algorithm Combined Semifragile Watermarking with Compressive Sensing, in Intelligent System Design and Engineering Application (ISDEA), 2012 Second International Conference on , vol., no., pp.134-137, 6-7 Jan. 2012. [2] Y. Shi, M. Qi, Y. Yi, M. Zhang and J. Kong, Object based dual watermarking for video authentication, Int. J. Light Electron Opt. , vol. 124 , no. 19 , pp.3827 -3834 , 2013. [3] M. Fallahpour, S. Shirmohammadi, Semsarzadeh and M. Jiying Zhao, Tampering Detection in Compressed Digital Video Using Watermarking, Instrumentation and Measurement, IEEE Transactions on , vol.63, no.5, pp.1057-1072, May 2014. [4] R.D. Patil and S. Metkar, Fragile video watermarking for tampering detection and localization, Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on , vol., no., pp.1661-1666, 10-13 Aug. 2015. [5] C. Ingemar, M. Matthew, B. Jeffrey, F. Jessica, K. Ton, Digital Watermarking and Steganography (ED.2, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA,2007). [6] J. Vandewalle, and B. De Moor, A variety of applications of singular value decomposition in identification and signal processing, SVD and Signal Processing E. Deprettere (ed.), North Holland, 1988, pp. 4391. [7] S. Kapre Bhagyashri and M.Y. Joshi, Robust image watermarking based on singular value decomposition and discrete wavelet transform, Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on , vol.5, no., pp.337-341, 9-11 July 2010. [8] H. Douzi, D. Mammass, F. Nouboud, Faber-Schauder wavelet transformation application to edge detection and image characterization, Journal of Mathematical Imaging and Vision Kluwer Academic Press,Vol. 14, n. 2, pp. 91-102,2001. [9] W. Sweldens, The lifting scheme, A construction of second generation wavelets, SIAM Journal onMathematical Analysis, vol. 29, n. 2, pp. 511-546, 1998. [10]S. Szenasi , Distributed Region Growing Algorithm for Medical Image Segmentation, International Journal of Circuits, Systems and Signal Processing, Vol. 8, n. 1, pp.173-181,2014. [11]J. Canny, A computational approach to edge Detection, in Pattern Analysis and Machine Intelligence, IEEE Transactions, pp. 679-698, 1986. [12]M. El Hajji, H. Douzi, D. Mammas, R. Harba, F. Ros, A New Image Watermarking Algorithm Based on Mixed Scales Wavelets, J. Electron. Imaging , Vol. 21, n. 1.2012. [13]A. Azeroual and K. Afdel, Low Complexity Image Authentication Based on Singular Value Decomposition and Mixed Scales Faber Schauder Wavelet, in International Review on Computers and Software, Vol.10, n. 12,pp.1209-1215, 2015.


[14]S.C. Byun., S.K. Lee., A.H. Tew-fik., B.H. Ahn., A SVD-Based Fragile Watermarking Scheme for Image Authentication, International Workshop on Digital Watermarking, LNCS 2613, (Page: 170-178, 2003). [15]N. Otsu, A threshold selection method from grey scale histogram, IEEE Trans. on SMC, Vol. 1, pp. 62-66, 1979. [16]M. Hajji, H. Douzi and R. Harba, Watermarking Based on the Density Coefficients of FaberSchauder Wavelets, Proceedings of the 3rd international conference on Image and Signal Processing, July 01-03, 2008, Cherbourg-Octeville, France. [17]A. Azeroual, K. Afdel, M. El Hajji and H. Douzi, On-line Key Frame Extraction and Video Boundary Detection using Mixed Scales Wavelets and SVD, International Journal of Circuits, Systems and Signal Processing, Vol. 9, pp.420-426, 2015. [18]S. Lei, G. Xie, and G. Yan, A Novel Key-Frame Extraction Approach for Both Video Summary and Video Index, The Scientific World Journal, vol. 2014, Article ID 695168, 9 pages, 2014.

Authors’ information A. Azeroual, received in 2012 the Master on Computer Systems and Networks from The University of IBN ZOHR Morocco. Since December 2012 she prepares Ph.D on Computer Systems and Vision.

K. Afdel, received in 1994 the Doctorat (French Ph.D) from the University of Aix Provence France in Computer Engineering, Analysis and Medical Image Processing. Since 1995 he is Professor at the University of Agadir, Morocco. His research interests are mainly on Computer Vision and Machine Learning.