A Robust Watermarking Method for Mpeg-4 Based on Kurtosis

Transcription

1 Advance Access publication on 24 October 2014 c The British Computer Society All rights reserved. For Permissions, please journals.permissions@oup.com doi: /comjnl/bxu112 A Robust Watermarking Method for Mpeg-4 Based on Kurtosis Murat esilyurt 1, ildiray alman 1, and A. Turan Ozcerit 2 1 Computer Engineering Department, Turgut Ozal University, Ankara, Turkey 2 Computer Engineering Department, Sakarya University, Sakarya, Turkey Corresponding author: yyalman@turgutozal.edu.tr Watermarking is a method of data hiding in relation to information security and copyright protection. In this paper, a new kurtosis-based watermarking approach is proposed for compressed video files. The proposed approach has two main parts. First, an intraframe of the video file is transformed from RGB to CbCr color space and each component s kurtosis value is calculated. A threshold kurtosis value is determined for the video because it is recommended to interpret the kurtosis values in combination with noise and resolution measurement. Secondly, the middle band Discrete Cosine Transform (DCT) coefficients of the luminance () component is used for watermarking processes considering the threshold kurtosis values. Each bit of the binary watermark is embedded in a different DCT block. The experimental results show that the proposed method can be safely used for watermarking processes when the point of interest is binary watermarks. In addition, the proposed watermarking approach is highly imperceptible and robust. Statistical and perceptual quality evaluations show that the proposed approach is better than its counterparts. 1. ITRODUCTIO Digital watermarking is a novel technique developed to overcome the problem of copyright protection of digital content [1]. A lot of watermarking methods have been developed in the last decade, because widespread applications in conjunction with the increasing speed of the Internet have resulted in an increase in the illegal use of digital content [2]. In general, watermarking schemes can be classified into two methods for data hiding in compressed video streams. The scheme in the first method is to fully decompress the video, embed the watermark into the raw video and then finally recompress the video data again. This approach s disadvantages are additional compression noise and a degradation of video quality during the re-compression process. Moreover, a binary watermark is not robust against attacks [3]. It requires fully decompressing for watermark-embedding and then, recompressing the watermarked video stream again. Another method is to embed and detect directly in the compressed video stream to avoid computationally demanding operations [4]. Motion Vector (MV) domain, Variable Length Code (VLC) domain and some transform (i.e. Discrete Cosine Keywords: Mpeg-4; compressed video stream; Kurtosis; watermarking Received 27 February 2014; revised 2 October 2014 Handling editor: Suchi Bhandarkar Transform (DCT), Discrete Wavelet Transform, Discrete Fourier Transform) domains have been used for this dataembedding approach. Xu et al. [5] have proposed that MV information can be selected for watermark hiding and extraction processes while control information is embedded to the I-frame in each group of pictures. In another approach, Fang and Chang [6] have used MV in macroblocks interframe (P and B frame) in the video stream for embedding data. Langelaar et al. [7] have proposed a scheme where data are embedded by modifying the code generated by VLC. Furthermore, Pranata et al. s [8] method proposes that data embedding is achieved by associating bit lengths of a set of multiple watermarked VLC code words. Many watermarking techniques use the DCT domain, which has three regions, Low Frequency (DC), Mid-Band Frequency and High Frequency (AC). Hsu and Wu [9] have proposed the raw video watermarking matter by a DCT transform on a block-by-block basis in a video stream. Similarly, a robust watermarking method has been proposed in DCT coefficients for both JPEG and MPEG standards [10]. Langelaar and

2 1646 M. esilyurt et al. (a) (b) M n n+1 n+2 n+3 t Lagendijk s [11] method has proposed to embed the watermark to the Intra (I) frame of the MPEG video stream. It is based on selectively discarding high-frequency DCT coefficients in the compressed data stream. Sarkar et al. s [12] method has proposed a high-volume transform domain data hiding in MPEG-2 video files. They have applied Quantization Index Modulation to low-frequency DCT coefficients and adapted the quantization parameter based on MPEG-2 parameters. Furthermore, they have varied the embedding rate depending on the type of frame. Barni et al. s [13] method has proposed a watermarking method for MPEG-4 embedding in some middle frequency DCT coefficients for the luminance blocks of pseudo-randomly selected macroblocks. Besides, Alattar et al. [14] have proposed the spatial spread-spectrum watermark, which is embedded directly to compressed MPEG-4 bit streams by modifying DCT coefficients. These days, H.264 or (Advanced Audio-Video Coding/ Decoding Standard) AVC is a relatively new standard for video compression like MPEG-4 structure. H.264/AVC is a hybrid video coder; intra- and Interprediction are used together to exploit spatial and temporal redundancies, respectively [15]. Tang et al. [16] have proposed that data are embedded into DC coefficients of 4 4 macroblocks inside the I- frame of H.264/AVC. Besides, Tian et al. [17] propose Context Adaptive VLC (CAVLC)-based blind watermarking for H.264/AVC. It ensures that watermarking data are only embedded into the last non-zero non-trailing AC coefficients in CAVLC. From these points of view, a kurtosis-based watermarking approach is proposed in this paper for MPEG-4 video files. The proposed approach is based on directly embedding and directly detecting the watermark to the compressed video stream to avoid computationally demanding operations. The paper is organized as follows: Section 2 presents the structure of MPEG-4 and its details; Section 3 describes the statistical texture features of video frames; Section 4 presents the proposed watermarking method and its details (selection appropriate frame, embedding algorithm, use of the neighbor coefficients method and watermark extraction). Experimental FIGURE 1. I, P, B frames (a) and sample video sequence in time t (b). results and conclusions are presented in Sections 5 and 6, respectively. 2. STRUCTURE OF MPEG-4 Efficient video compression techniques remove spatial, temporal and perceptual redundancy from a video. Many digital video applications employ lossy compression techniques, such as MPEG-1, MPEG-2 and MPEG-4. Basically, the structure of the MPEG-4 standard is no different from other MPEG standards. They are all cosine-transform-based compression standards and constitute the same units, such as block-based motion compensation, motion-vector and motion quantization. In addition, MPEG-4 has an I (Intra) Frame, a P (Predictive) Frame and a B (Bi-directional) frame [17]. I, P and B frames in an MPEG structure and sample video sequence (M resolution) can be seen in Fig. 1. Each frame of input video sequence is segmented into a number of arbitrarily shaped regions, Video Object Planes (VOPs) and the shape, motion and texture information of the VOPs belonging to the same Video Object (VO) are coded into a separate Video Object Layer. The first VOP is coded as the I- frame (I-VOP coding mode) by splitting it into macroblocks. Each macroblock has a luminance () component and a chrominance (C) component in 8 8 pixel blocks. Each 8 8 block is produced as a DCT coefficient, and then quantized resulting in that block. Each subsequent VOP in the Group of Object Video is coded using P-VOP or B-VOP redundancy and then into blocks that are compressed in the same way as I-VOP blocks [12]. Video compression field tests of natural video at rates below 1 Mbps have consistently indicated better performance for MPEG-4 than for MPEG-1 and MPEG-2 [14]. 3. THE STATISTICAL TEXTURE FEATURES OF A FRAME The statistical texture features mean that standard deviation, skewness and kurtosis are calculated by using the probability distribution of intensity levels. Here, I is the matrix of

3 A Robust Watermarking Method 1647 Original Video Watermark Watermarked Video Is the Take a Kurtosis DCT EP Frame Encoding Value ()' Suitable? CT DCT IDCT ICT FIGURE 2. General scheme of the proposed kurtosis-based watermark-embedding approach. luminance () component of only one frame in an original video, and n is the matrix elements. Ī can be calculated as I = 1 n I i (1) n Mean value and standard deviation are directly related to each other. If available data are close to the mean, standard deviation is low, otherwise standard deviation is high. Standard deviation (stddev) is equal to 0 available data are distributed evenly (Eq. 2). ( ) 1/2 1 n stddev = (I i I ) 2 (2) n i=0 Skewness is a measure of the asymmetry of a distribution around its mean (Equation 3). It can be positive or negative. A negative skewness value means that the tail lies on the lefthand side of the probability density and the bulk of the values lie to the right of the mean; a positive skewness value is vice versa. Skewness = i=0 (1/n) n ( i=0 Ii I ) 3 ( (1/n) n ( i=0 Ii I ) ) 3 (3) 2 The fourth process, kurtosis, is a measure of peakedness of the available distribution of data (Equation 4). An image s kurtosis value is directly related to noise ratio and resolution. It is not required to have a very high value. Separation of rarefaction of the rise of the value of the distribution (deviation) indicates this. Kurtosis = (1/n) n ( i=0 Ii I ) 4 ( (1/n) n ( i=0 Ii I ) ) 4 (4) 2 The Kurtosis value is very important feature which is used for comparison of frames features for watermarking in the proposed method. The most widely used method, for comparison of two frames, is a method of examining histograms of frames. But the histogram method, mean, standard deviation and skewness values may be the same. On the contrary, only the kurtosis values are not the same. Specifically, differences in density of DCT coefficients can be understood by the presence of kurtosis values. Thus, providing a robust watermarking can be done [18]. In addition, kurtosis values are always positive values and greater than zero. Therefore, mathematical calculation is reduced and easier. 4. PROPOSED KURTOSIS-BASED WATERMARKIG APPROACH This section presents the proposed kurtosis-based watermarking approach for MPEG-4 video files. The binary watermarkembedding algorithm is based on DCT and middle band coefficients of the luminance DCT block and I-Frames are used for watermark bit embedding. Figure 2 presents a general scheme of the proposed embedding process, where CT, EP, DCT, IDCT and ICT represent Color Transformation (RGB to CbCr), Embedding Process, which is an unimportant step in the proposed method s base process (it is detailed in the following subsections), DCT, Inverse DCT and Inverse Color Transform, respectively Kurtosis-based suitable frame selection for watermarking Many researchers have used I-Frames of compressed videos for watermarking [19, 20]. The most important process for the proposed approach is suitable frame selection. The suitable frame selection and decision process are based on the kurtosis and it is the best method for achieving robustness. The kurtosis value of each frame gives resolution and noise ratio information about the frame. In Fig. 3, there are some versions of the test video s (Mobile.mp4) I-frames under attack. While the kurtosis value of luminance () component of the original I-frame is 3.28 (Fig. 3a), its distorted version s (salt and pepper noise) value is 3.31 (Fig. 3b). This means that the kurtosis value is increased when the frame is distorted.

4 1648 M. esilyurt et al. FIGURE 3. The kurtosis value of the original frame s component is 3.28 (a), the kurtosis value of the distorted (Salt and Pepper noise) frame is 3.31 (b), the kurtosis value is 3.29 after the original frame s resolution ( ) is changed to.sif format ( ) (c), the kurtosis value of the distorted (Gaussian noise) frame is 3.35 (d). The changing of the image resolution is another important factor that affects the kurtosis value. After the original frame (.cif file format, resolution) has been converted to the.sif file format ( resolution), the kurtosis value is 3.29 (Fig. 3c). Similarly, after Gaussian noise has been added to the luminance component of the original I-Frame, the kurtosis value is 3.35 (Fig. 3d). As mentioned previous paragraph, if noise ratio on the frame increases, the kurtosis value increases, too. This also indicates the abundance of the video frame s degradation. As mentioned above, our aim is to minimize data loss on the watermark when it is extracted from a distorted video frame. A kurtosis value of luminance component is more efficient in terms of noise ratio and unwanted changes. Thus, kurtosis values play a crucial role during frame selection for watermarking. If watermarking processes are applied on the frames without taking this into consideration, it will not be a robust watermarking scheme. The kurtosis value is used to prevent the probable loss on the watermark. All frames kurtosis values of Foreman (300 frames), Mobile (299 Frames), Tennis (150 frames) and Tempete (260 frames) test videos are shown in Fig. 4. Each video file has its own kurtosis value set. What is important here is the set of values in the general distribution of the values are outside. Because, the noise (i.e. jpeg compression, salt and pepper noise, Gaussian noise, etc.) on an image causes marginal change on the kurtosis value of the frame as seen in Fig. 3. Kurtosis values of all the frames for Foreman are seen as a cluster between 10 and 40 (Fig. 4a). Similarly, kurtosis values of all the frames for Mobile, Tennis and Tempete are seen as a cluster between 0 and 5, 0 and 2, and 0 and 20, respectively (Fig. 4b d). Thus, it can be said that some frames where the kurtosis values are outside of the cluster should be ignored for robust watermarking because they have marginal kurtosis values (Fig. 3). Considering the fact that the frames which have marginal kurtosis values are more distorted than the other frames Watermark-embedding and extraction processes After kurtosis-based suitable frames are selected for watermarking, the binary watermark is embedded to DCT blocks of

5 A Robust Watermarking Method 1649 (a) 180 Kurtosis Values (b) Kurtosis Values (c) Kurtosis Values (d) Kurtosis Values FIGURE 4. Kurtosis values of each frame for Foreman (a), Mobile (b), Tennis (c) and Tempete (d).

6 1650 M. esilyurt et al. I-frames because it is usually used for robust embedding processes in compressed videos. Watermark-embedding steps are given as follow: (iii) Appropriate I-Frames that are inside the threshold values? are used for the watermark-embedding processes as detailed below. (i) At first, the watermark-embedding process starts with decoding the original video and each I-frame is transformed from RGB to CbCr color components. (ii) All components kurtosis values are calculated. These kurtosis values are saved for a decision, which means determination of threshold kurtosis values and selection of the appropriate I-frames for watermarking. D 2 + C D2 >D 1 SWAP D2, D1 W(i)=1 D 1 >D 2 SWAP D1, D2 FIGURE 5. The flow chart of EP [20 22]. Is the D 1 + C As previously mentioned, a new embedding approach has been developed for robustness, inspired by the method detailed in [20 22]. The method can embed a bit in each DCT block. Let us assume that the selected frame size is M and the luminance component () is divided by 8 8 blocks for the watermark-embedding process. Block numbers in image (I bn ) can be calculated as seen in Equation (5). Then, each block of component is separately transformed to 2D-DCT after color transformation. I bn = (M )/64 (5) It is important to note that the watermark is embedded several steps into the cover frame. The steps depend on the size of the selected frame and watermark. In the embedding process, each binary watermark bit has been embedded using DCT(5, 2) and DCT(4, 3) coefficients of the mid-band frequency fields of the luminance () component. In this study, DCT(5, 2) and DCT(4, 3) coefficients are referred to as D 1 and D 2, respectively. Take a Kurtosis Frame Value DCT WE () Encoding Suitable? CT ICT FIGURE 6. WE processes. Watermark (a) (b) FIGURE 7. DCT coefficients of Mobile (a) and Foreman (b) video frames after watermark bit (1) 2 embedding (D 1 > D 2 ).

7 A Robust Watermarking Method 1651 (a) (b) FIGURE 8. DCT coefficients of Mobile (a) and Foreman (b) video frames after extracting the watermark bit (D 2 > D 1 ). FIGURE 9. Defective block numbers for I-Frames in which the kurtosis value is outside the threshold values. TABLE 1. The number of DB and DB K for the reference videos I-frames. Sample Video Frame Acquirement video format size DB DB K performance (%) Foreman MPEG R Hall Monitor MPEG R Mobile MP4 R Tennis MP4 R Foreman Mp4 R Tempete Mp4 R Tennis Mp4 R The proposed method can be applied without the process of adding or subtracting to the coefficients of each DCT located in the blocks (8 8) unlike many others (Fig. 5). While the Embedding Process (EP) is realized into each block, if the watermark bit is equal to 1, D 1 is expected to be larger than D 2. If the data equals 0, then D 2 is expected to be larger than D 1 (Equation 6)[20 22]. { } D1 > D 2 1 w(i, j) = D 1 < D 2 0 The DCT coefficients may become very close to each other depending on the structure of the cover images during the EP. That is why a constant C is defined to prevent possible losses when the watermark is extracted (Fig. 5) [20 22]. This way, watermarked coefficients become less affected from the distortions and attacks by the DCT and IDCT processes. During the extraction process, DCT(5, 2) and DCT(4, 3) watermarked coefficients are referred to as D 1 and D 2, respectively, and watermark bits are extracted as seen below (Fig. 6). Embedded data are extracted without using the original host/cover video file. During the first step of the extraction process, the watermarked video s frames are separated and then, it is converted from RGB to CbCr color space. Luminance () component s DCT coefficients are calculated for each 8 8 block. D 1 and D 2 are used for obtaining the watermark bit. According to the relationship between these coefficients, one bit from each block is obtained. The binary (6)

8 1652 M. esilyurt et al. watermark extraction (WE) step can be seen in Fig. 6 and (Equation 7) [20 22]. { w 1 D (i, j) = 1 > D 2 } 0 D 2 > D 1 (7) 4.3. The defective block In frequency domain watermarking, compressed video frames or images are used as a cover object and distortions are unavoidable. These distortions may be performed during the data-embedding process, quantization, and attacks during or through outside intervention. As it is known, using the DCT coefficients during the data hiding process may increase data loss on cover files. In addition, if the data is embedded into the DCT coefficients by adding or removing, this process causes more distortions on the blocks that are in the I-frames. Thus, lossless WE from these blocks (defective block) is impossible. All watermarking methods must take into account this important complication. A solution is proposed for this problem in this paper. First of all, kurtosis values are calculated for all I-frames and a range in which results are concentrated is determined. Thus, the most suitable frames are determined before the watermarking process. In other words, it can be said that there is a relationship between kurtosis values and whether the block is defective or not. Figure 7 shows that DCT coefficients of Mobile (a) and Foreman (b) video frames after watermark bit (1) 2 embedding. Figure 8 shows DCT coefficients of Mobile (a) and Foreman (b) video frames after extracting the watermark bit (0) 2. It means that these blocks are defective and the watermark-embedding process is unnecessary because these frames kurtosis values are out of the defined kurtosis range. 5. EXPERIMETAL RESULTS An important issue in comparing the proposed watermarking method with its counterparts is the peak signal to noise ratio (PSR) performance with respect to the maximum watermarked bits-embedding capacity. PSR measurement is used to evaluate the watermarked frame quality with respect to the cover frame as a statistical measure during experimental works. A computed PSR value indicates the quality approximation between a watermarked frame and the original. The higher the PSR value is, the better the watermarking process is in terms of HVS perception [2]. In addition, to analyze performance of the proposed method, widely used video files in the literature and other comparison metrics (ormalized Cross-Correlation (CC), Bit-Rate Increase Ratio (BIR), Mean Structural Similarity (MSSIM)) are used. In this scope, performance analysis of the presented method and its results are given in the following paragraphs. FIGURE 10. Original frames (Mobile (a), Foreman (c), Hall Monitor (e), Tempete (i), Tennis (c)) and their watermarked versions (Mobile (b), Foreman (d), Hall Monitor (f), Tempete (h), Tennis (j)). Some reference video files (i.e. Hall Monitor, Foreman, Mobile, Tennis and Tempete) have been used as test video files for experimental applications. Foreman (300 frames), Mobile (299 frames), Hall Monitor (299 Frames) all have 30

9 A Robust Watermarking Method 1653 frames per second (fps) video files and they have a resolution, which is notated as R 1 below. Tennis (150 frames) has a resolution, which is notated as R 2, Tempete (260 frames) has a resolution, which is notated as R 3 and they have 30 fps, like the others. The experimental results show that the number of defective blocks (DB) of an I-frame that is casually watermarked is more than the number of DB of a watermarked I-frame that is selected using kurtosis (DB K ) as seen in Table 1. It can be easily said that the presented kurtosisbased watermarking approach s performance is better than the classical watermarking approach. This means that the proposed approach is more robust than the classical watermarking approach. Figure 9 shows the I-frame numbers of reference video files and the number of defective blocks of I-frames. Because those defective blocks cause loss of hidden data, they are excluded from the embedding process. As seen in Fig. 9, there are 350 defective blocks for the 192th I-Frame of the Foreman video. This means that the data are embedded on this frame and the data cannot be properly extracted. Thus, the robustness requirement will not be achieved. Original test video frames ((a), (b) and (c)) and their watermarked versions ((d), (e) and (f)) coded by the proposed approach are seen in Fig. 10. Table 2 shows the experimental average -PSR results for each watermarked I-frame. The results show that the proposed approach s performance is better than the counterpart [23] and [24] in terms of PSR. Similarly, the number of watermarked bits and PSR results are presented in Table 3. It shows frame sizes (R 1 ) of cover videos, embedded bits and PSR results. The results show that the proposed approach s performance is better than the counterpart [24 26] in terms of PSR and watermark bits. TABLE 2. -PSR values for intraframes of sample videos. Sample Average Liu et al. Wang et al. video -PSR (db) [23] [24] Foreman db db Mobile db db Tennis db Unused Tempete Unused db It can be easily seen that the proposed method s performance is very efficient for different frame sizes. The proposed method s all counterparts [24 26] have been realized on different frame sizes R 1. The PSR results show that performance of the presented method is better than its all counterparts. In addition to the results presented above, some attacks (Sharpness, Salt and Peppers noise, Jpeg Compression and Gaussian noise) have been performed on the watermarked I-Frames of Foreman video for robustness measuring. Extracted watermarks are shown in Fig. 11 after the attacks. Another performance evaluation metric used for experimental performance evaluation is CC. A computed CC value indicates the quality approximation between a recovery watermark and the original watermark. The CC can be computed as given in Equation (8). Where I and I are the original and watermarked frame values and the frame size is M. M M CC = I j,k I j,k (I j,k ) 2 (8) j=1 k=1 j=1 k=1 For example, the CC results of the proposed method for Foreman video are , , , , , , , , and after the Sharpen attack, Salt and peppers (0.05), Salt and peppers (0.02), Salt and peppers (0.01), the JPEG Compression (quality 90, 50 and 30), Gaussian noise (0.01), Gaussian noise (0.02) and Motion Blur attacks, respectively (Table 4). In addition, additional CC results have been presented for other reference video files (Mobile and Tennis) in the Table 4. These results show that the presented method guaranteed the CC values higher than 0.92 and it is extremely robust against the attacks mentioned above. Since the bit-rate increase depends on the embedded capacity, we define the BIR (Equation 9) as the percentage of bit-rate increase per embedded bit in order to perform a fair comparison (Table 5). BR(Watermarked) BR(Original) BIR = ( 100) (9) Embedded Capacity BR(Original) where BR (Watermarked) and BR (Original) are the number of bits utilized for coding the watermarked and original sequences, respectively [27]. Table 5 shows the comparison of the BIR (BIR 10 3 )of different video sequences in the proposed method. The results show that the proposed method can prevent the bit-rate increase for most of the video sequences. TABLE 3. Comparisons on the watermarked bits and PSR results. Performance Proposed Kim et al. Proposed Wang et al. Wu et al. Frame size metric method [25] method [24] [26] R 1 ( ) Watermarked bits PSR (db) db db db db db

10 1654 M. esilyurt et al. FIGURE 11. Extracted watermark after the attacks: Original watermark (a) Sharpen(b), Salt and peppers (1%) (c), Salt and peppers (2%) (d), Salt and peppers (5%) (e), Jpeg compression (10%) (f), Jpeg compression (30%) (g), Jpeg compression (50%) (h), Gaussian noise (1%) (i), Gaussian noise (2%) (j), Motion Blur (k). TABLE 4. CC values of watermarks against some attacks for the proposed method. Attacks Foreman Mobile Tennis Without attacks Sharpen Salt and peppers noise (0.05) Salt and peppers noise (0.02) Salt and peppers noise (0.01) JPEG compression (Quality factor: 90) JPEG compression (Quality factor: 50) JPEG compression (Quality factor: 30) Gaussian noise (0.01) Gaussian noise (0.02) Motion Blur TABLE 5. Different video sequences and BIR values. Video Frame Watermarked sequence size bit I-frame BIR 6. COCLUSIOS As it is known, data hiding methods in compressed videos that use DCT and high compression rates may suffer from data loss. In addition to this, if the data are embedded into the DCT coefficients by adding or removing, this process causes more distortions on the blocks that are in I-frames. Thus, lossless WE from these blocks (defective block) is too difficult, if not impossible. All watermarking methods must take into account this important complication. The proposed approach s results show that there is a very close relationship between kurtosis values and the number of defective blocks. Thanks to ranges of kurtosis values specified separately for each video file, the number of defective blocks can be easily reduced. When the range of kurtosis values between 2 and 5 is determined for the Mobile video file, the number of defective blocks that are used for watermarking is reduced by 17 18%. Similarly, when the range of kurtosis values between 10 and 40 is determined for the Foreman video file, the number of defective blocks that are used for watermarking is reduced by 14 18%. Specified ranges can be different for each file or I-frame, but these ranges must be known while the WE process is applied. Foreman R ews R Tennis R Tennis R Tempete R Mobile R ACKOWLEDGEMET The authors thank the anonymous reviewers and the editor for their valuable comments and suggestions for better presentation of this work.

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