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An improved and secure digital video watermarking technique
1Jagminder Singh Gondara, 2Anupama Gupta, 3Pavninderpal Kaur
Abstract— In this paper, an improved approach for invisible digital video watermarking is proposed. Digital video watermarking is mainly done for copyright protection in which secret information is embedded in the video. In this method, singular value decomposition (SVD) is applied on the sub-bands, which are achieved by the decomposition of randomly selected frames from the video by applying 2-level discrete wavelet transform on them. Then, SVD is also applied on watermark image and SVD applied watermark image is embedded in the chosen host video frames.
Robustness of the proposed algorithm has been tested through various metrics like correlation coefficient, peak signal to noise ratio, bit error rate and structural similarity index matrix. The result values reflect an average increase of 13.02% in peak signal to noise ratio values and 8.87%
average increase in correlation coefficient values.
Index Terms— Digital watermarking, discrete wavelet transform, singular value decomposition, security
I. INTRODUCTION
Due to rapid growth of intent technologies as well as advancements of digital multimedia processing, bulk amount of data is just a click away to everyone in these days. But this growing technology also facilitates unauthorised copying, tempering and piracy of digital video. The ease with which these illicit activities are done, laid emphases on the need of data authentication techniques [1] to protect the intellectual property rights of the owner of digital work. Digital watermarking is defined as the phenomenon of embedding image, text, etc into the multimedia elements such as image, video, etc. to protect copyright information [2].
The general model of digital watermarking is shown in Fig.1. In generation stage, first watermark creation is done carefully with unique and complex contents in such a way that it is difficult for someone to extract or damage it with possible attacks [3]. In embedding stage, embedding of watermark in cover media is done. At the extraction stage, the reverse of the embedding algorithm is done to extract the watermark. Equation 1 shows embedding, where WI is watermark image, CI is original image, E is embedding function and WM is watermarked media [4].
WM= E (CI, WI) (1)
Figure 1: General model of digital watermarking.
In this paper, an effective video watermarking algorithm, in terms of robustness and imperceptibility, is proposed by using two mathematical transforms, i.e., discrete wavelet transformation (DWT) and singular value decomposition (SVD). These are explained as follows:- Discrete Wavelet Transformation: DWT is multi-resolution description which decomposes the signal into mutually orthogonal set of wavelets. In DWT, decomposition of an image or video frame is done into four sub images out of which three are details and one is approximation [4]. Approximation (LL) part is low frequency part and the detailed parts are comparatively high frequency parts i.e. horizontal details (LH), vertical details (HL) and diagonal details (HH). Fig. 2 shows the level 1 DWT where LL part is again decomposed, and this resultant is called level 2 DWT shown in Fig. 3, because low frequency parts are easy to split in comparison to high frequency parts.
Figure 2: 1-Level DWT.
Figure 3: 2-Level DWT.
Singular value decomposition: Singular value
decomposition is a factorization of complex matrix into three singular matrices with useful applications [5].
SVD is explained for any real matrix A where A is m*n matrix which is decomposed uniquely by SVD as follows:
A = U S VT (2)
Where U is m * n matrix such that U=AAT, V is n * n matrix such that V = AT A and S is n * n matrix such that S = diagonal (σ1,σ2,σ3,…….., σn) (3)
where σ denotes eigen values. These three matrices U, S and V are singular matrices.
SVD technique is a better technique because the size of matrices in this is not fixed. It can rectangular or square.
The result of SVD is singular matrices and the singular matrices are least affected by any type of processing or attack applied on them [6]. The remaining paper is organized in different sections. The related work and proposed work are described in section II and section III respectively. Section IV covers results and discussion.
Conclusions and future scope of proposed work are given in Section V.
II. RELATED WORK
Hartung and Kutter [5] reviewed the requirements and applications of watermarking. Few remarks were made about the future developments in the field of watermarking. Zhu et al. [7] proposed an algorithm using two and three dimensional discrete wavelet transform for watermarking of images and video frames. Houmansadr and Ghaemmaghami [6] introduced a technique of embedding a watermark in the video sequences based on visual cryptography and performed temporal scrambling for watermark embedding. Mansouri et al. [8] proposed non-blind image watermarking using SVD in complex wavelet transform (CWT) domain because real wavelet domain provides lower capacity then CWT. Gopal and Latha [4] presented a watermarking technique in which BT.656 raw digital video stream was used and watermark pattern was embedded in that and implemented in a hardware, namely, Field Programmable Gate Array.
Authors used real time authentication in their method.
Yassin et al. [9] proposed DWT based video watermarking scheme in which binary watermark image was embedded in frames of video. The watermark was embedded into PCA block of maximum coefficient of the selected bands. Bhatnagar and Raman [3] proposed a robust video watermarking algorithm which was based on wavelet packet transform. Chimanna and Khot [10]
proposed a 2 level DWT video watermarking technique which used principal component analysis. Agilandeeswari and Muralibabu [2] proposed a video watermarking scheme which used discrete wavelet transform and singular value decomposition. It is based on a procedure
in which sub-band selection was done. Two watermarks were used, where first was the original watermark and second was owners’ fingerprint to increase authentication level.
III. PROPOSED WORK
In this section, an improved approach to video watermarking is proposed. This approach is split into two phases, first is embedding phase and second phase is extraction phase. This whole work is done in MATLAB tool and version used is R2010a.
A. Embedding phase: In the embedding phase, first the video is divided into frames. After this, ten frames are selected randomly on the basis of the secret key entered by the user. Then, DWT is applied on a selected frame to get LL, LH, HL and HH parts of frame. This is followed by application of SVD on LL part of frame to get U, S and V. Subsequently, SVD is applied on the watermark image to get Uw, Sw and Vw components of watermark image.
Then, Sw and S are added to get the watermarked S (say WS). After this, apply inverse SVD on U, WS and V to get LLw (watermarked LL). The inverse DWT is applied to get watermarked frame. This procedure is repeated for all the ten selected frames to get ten watermarked frames.
In the end, frames are rearranged and converted to video to get the watermarked video.
Figure 4: Flowchart of embedding phase.
B. Extraction phase: In this phase, extraction of watermark is done from the watermarked video. First, the watermarked video is selected and key is entered which must match with the key entered at the time of the watermark embedding. If key does not match with the original key then the extraction procedure cannot be done.
When key is matched, the ten watermarked frames are
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selected on the basis of secret key entered which are same with the frames which were selected at the time of watermark embedding and which contains watermark in them. This is followed by application of 2-level DWT on the selected frames. Then, SVD is applied on LL part of 2-level DWT resultant from the last step. The singular values of watermark are calculated from singular values obtained from last step. From these singular values, the watermark is extracted. After this, quantitative and qualitative comparison of extracted and original watermark is done and results are assessed.
Figure 5: Flowchart of extraction phase.
The above mentioned technique is expected to be more robust with high quality and less perceptibility. Extraction procedure is simply the reverse of embedding procedure.
IV. RESULTS AND DISCUSSION
The proposed watermarking technique is tested on a standard video from MATLAB library, namely, rhinos.avi. Total number of frames of video are 114.
Frame rate of video is 15 frames/second. Format of video is RGB. Total time duration of video is 7.6 seconds.
Watermark image used is LLRIET.jpg. It is a gray scale image and resizing is done before using as watermark.
The dimension of image which is used as watermark is 128 × 128. Secret key used for testing is 1234567890 for all results. Fig. 6 shows the original watermark image used for embedding. On the basis of secret key used the selected frames are shown in Fig. 7.
Figure 6: Watermark image.
Figure 7: Original frames selected on the basis of secret key used.
Quantitative evaluation: In this section, quantitative evaluation of work is done using four metrics namely, peak signal to noise ratio (PSNR), bit error rate (BER), structural similarity index matrix (SSIM) and mean square error (MSE). Table I shows the values of these metrics for the extracted watermark which survived under various attacks like poisson noise, speckle noise, Gaussian noise, salt and pepper noise, etc. These attacks were applied during the extraction procedure. The quantitative validation of the proposed algorithm reveals that the proposed algorithm shows the promising results with most of the attacks used.
Qualitative evaluation: Human perception played a vital role in the qualitative analysis of the proposed work.
Table II reveals the original and extracted watermark images which survived under various attacks applied while the extraction procedure and shows the impact of applied attack on the watermark. In most of the cases, the extracted watermark survived under attacks.
Table I: The values of extracted watermark calculated in comparison to the original watermark
Attack Type PSNR CC BER
Without attack
37.8933 0.99497 0.02678 Poisson
Noise
30.5727 0.34078 0.03273 Salt &
pepper noise
32.3229 0.98271 0.03095
Gaussian noise
34.0253 0.97923 0.02941
Median filter 31.9791 0.97033 0.03128
Speckle noise 36.0314 0.98594 0.02795
Blur video frames
30.9648 0.83080 0.03229
Scaling attacks
32.6961 0.98630 0.03061
Table II: The extracted watermark after applying various attacks while extraction.
Attack Type
Original watermark Extracted watermark Without
attack
Speckle noise
Salt &
pepper noise
Gaussian noise
Median filter
Poisson noise
Blur video frames
Scaling attack
Comparison with existing technique: The proposed work is compared with the existing technique on the basis of two parameters namely, PSNR and CC. Table III shows the PSNR and CC values of the extracted watermark by the existing algorithm and the proposed algorithm.
Table III: Comparison of existing algorithm with proposed watermarking algorithm
Types of attacks
Existing watermarking
algorithm [2]
Proposed watermarking
algorithm Average
PSNR in
‘db’ CC
Average PSNR in
‘db’ CC
Gaussian
noise 29.1654 0.7387 34.0253 0.9792 Poisson
noise 24.5402 0.5025 30.5899 0.6025 Salt and
pepper noise
25.2658 0.8305 32.3505 0.9821
Median
filter 38.6543 0.8188 39.9791 0.9703
Table III shows better results of the proposed algorithm over the existing watermarking algorithm and reflects an average increase of 13.02% in PSNR values and 8.87%
average increase in CC values and shows that it is better than the existing algorithm.
From the following bar graphs shown in Fig. 8 and Fig. 9, it is very clear that the proposed work show promising results as these graphs show the average increase in the PSNR value and CC value for different attacks applied and is compared with the existing work. Fig. 8 shows increase in PSNR values and Fig. 9 shows increase in CC values.
Figure 8: Bar graph revealing comparison of PSNR values for existing and proposed work.
Figure 9: Bar graph revealing comparison of CC values for existing and proposed work.
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The results shown in Tables I, II and III clearly indicate that the performance of the proposed algorithm is good in terms of all the metrics and it is quite evident that algorithm is robust against several attacks.
V. CONCLUSIONS AND FUTURE SCOPE
A robust video watermarking algorithm is presented and two mathematical transformations are used in it, namely singular value decomposition and discrete wavelet transformation. This approach is semi-blind approach in which invisible watermark is used for embedding in ten randomly selected frames. The experimental results shows promising results for video watermarking and against attacks like poisson noise, Gaussian noise, salt and pepper noise, median filter, speckle noise, scaling attack, etc.
The same watermark is embedded ten times in ten randomly selected frames based on the entered secret key.
The watermark is extracted in extraction phase and different attacks are applied while extraction for testing the capability of the algorithm. Results are validated using different metrics like PSNR, CC, SSIM and BER. Results are validated qualitatively also through visual inspection scheme. This approach reflects an average increase of approximately 13.02% increase in PSNR values and 8.87% average increase in CC values from the existing technique of watermarking.
For the future work, multi level watermarking or colored watermark can be used for watermarking and embedding it on all video frames. The 3-level DWT may be used for embedding watermark in algorithm.
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