Address for Correspondence

Research Article A ROBUST WATERMARKING APPROACH FOR RAW VIDEO AND IT S DSP IMPLEMENTATION 1 Deepa Satish Khadtare, 2 Prof.M.M.Jadhav, 3 Mr.Mahesh Khadtare Address for Correspondence 1 M.E.Electronics (Digital system), Department of Electronics Engineering, Sinhgad College of Engineering. Pune University Pune, Maharashatra, India, Pin-411041. 2 Assistant Professor, Department of Electronics Engineering, Sinhgad College of Engineering. Pune University Pune, Maharashatra, India, Pin-411041. 3 M.Tech. (Digital signal processing), IIT Guwahati ABSTRACT Digital watermarking technique is a process of embedding an unperceptive signature or a copyright message such as a logo into a digital image. The advantages of watermarking are its imperceptibility and robustness. In order to protect original data, watermarking is first consideration direction for digital information copyright. In addition, to achieve high quality image, the algorithm maybe cannot run on embedded system because the computation is very complexity. In this paper, we propose a novel Discrete wavelet transform (DWT) based watermarking techniques algorithm which efficient inserts watermarking on digital image and very easy to implement on digital signal processor. The implementation work is carried on Blackfin DSP processor. In further, we select a general and cheap digital signal processor which is made by analog device company to fit watermarking application. The experimental results show that the video frame quality by watermarking insertion can achieve an average of 38 db after undergoing image processing, geometric transformation attacks, video frame watermarking attacks can be accepted in human vision and the extracted watermark is still recognizable. KEYWORDS Video Watermarking, DWT, Digital Signal Processor, PSNR. 1. INTRODUCTION By internet progress, the copyright topic becomes an essential issue. From signal point to expend to multimedia application such as image, audio and video; user need an efficient method to protect them authority, therefore watermarking is appropriate method. Digital watermarking has emerged as a potentially effective tool for multimedia copyright protection, authentication and tamper proofing [1]. The important terminologies pertaining to digital video watermarking [1] are digital video payload, perceptibility, bit-stream watermarking and robustness. So, any image watermarking technique can be extended to watermark videos, but in reality video watermarking techniques need to meet other challenges than that in image watermarking schemes. Watermarking is the process of inserting hidden mark in an image by introducing modifications to its pixels with minimum perceptual disturbance. A watermarking algorithm, originally conceived for still images application, has been extended to raw video, treating it as a set of single still frames [2]. Each frame of the video is transformed to wavelet domain by DWT. Based on the watermark image, random sequence is generated and embedded into the mid-frequency DWT coefficients. Watermark which is a logo is scrambled before embedding to increase its robustness. The robustness of the approaches is verified by the conduct of various attacks on the watermarked video frame and is compared with original video frame. The embedded information or watermark can be a serial number or random number sequence, ownership identifiers, copyright messages, control signals, transaction dates, information about the creators of the work, bi-level or gray level images, text or other digital data formats. In the literature [3], various invisible robust digital video watermarking techniques such as spatial domain, frequency domain and MPEG coding for secure multimedia creation and delivery is explained. Most of Video watermarking scheme is based on the techniques of the image watermarking. But video watermarking introduces some issues which not present in image watermarking [4]. Apart from robustness, reliability, imperceptibility, practicality, and video watermarking algorithms should also address issues such as localized detection, real time algorithm complexity, synchronization recovery, effects of floating point representation, power dissipation etc. It is been concluded that Video signals are highly susceptible to pirate attacks, which includes frame averaging, frame dropping, frame swapping [5]. The DWT based video watermarking algorithm [6] is implemented on DSP processor. Therefore an attempt to prove, the robustness evaluation for video watermarking as an essential ingredient [7] is explored in this paper. Papers [8],[9] is useful for selection of DSP processor and implementation of the DWT based video watermarking algorithm on DSP processor. Manual [10] is useful for DSP-BF561 Blackfin Processor hardware reference. The rest of this paper is organized as follows. Section 2, describes DWT based frequency domain watermarking technique. In section 3, we introduce the digital signal processor (DSP) which calls as Blackfin561. An overall watermarking insertion and extraction flow algorithm and how to implement algorithm on DSP system are depicted in

section 4. The section 5 descripts experimental environment and results. A conclusion is drawn in last section. 2. DWT BASED FREQUENCY DOMAIN WATERMARKING TECHNIQUE Our algorithm includes two backbones to structure a fully system which are watermark embedding and watermark extraction. As ADSP-BF561 is dual core, we need the core A is assigned to process the Watermark Embedding routine; afterward the core B will process Watermark Extraction routine. Finally, a well-known concept is that frequency domain can efficient embed watermarking and human vision cannot distinguish the image has be changed. Due to wavelet transform has self-similarity property which can cooperate watermarking. DWT includes coexist property of temporal and spatial. Afterward, the image through DWT is separated into four sub-images which locate on LL, LH, HL and HH band in figure 1. Beside, the sub-image in LL band is similarity with original image. In figure 2, show that the video frame of DEEPAL through 1-order DWT. Wavelet transform is a sequence of low pass and high pass filtering. Filtered signal is down sampled by 2 at each level and filtering is recursively applied on the lower passed signal. Later the DWT coefficient would be modified according to the watermark. DWT is chosen as it is more computationally efficient than the sinusoidal based discrete transform. The speed is faster than DCT and DFT as only sum or difference of the pixel have to be calculated. Thus DWT is selected on our algorithm system architecture. Figure 1: The DWT scheme based on 2-D and 1-order operation Figure 2: The result of 1-order DWT, form left-upper to right-down by zigzag order depicts the LL, HL, LH and HH bands. 2.1 Haar Wavelet Transform

The Haar transform is the simplest of the wavelet transforms. This transform cross-multiplies a function against the Haar wavelet with various shifts and stretches. The technical disadvantage of the Haar wavelet is that it is not continuous, and therefore not differentiable. Figure 3 shows Haar wavelet. Figure 3: The Haar Wavelet The Haar wavelet's mother wavelet function ψ(t) can be described as 1 ψ( t) = 1 0 0 t< 1/ 2, 1/ 2 t< 1, otherwise. and its scaling function φ(t) can be described as 1 0 t < 1, ( t) = 0 otherwise. (1) φ (2) 2.2 The One Dimensional Haar Transform While it is very important to keep in mind that the wavelet transform can be described by a unitary matrix, it is not efficient to perform the transformation by multiplying the matrix to a vector. The way to wavelet transform numerically, is to proceed as moving the filters ( 1/ 2, 1/ 2) and ( 1/ 2, 1/ 2) along the signal recursively. Program a function with the following in/output: Input: 1. A vector of length 2 k where k is a positive integer. 2. The ``level'' of decomposition. Output: A vector which is the Haar transformation of the vector you gave as input corresponding to the level you gave as an input. 2.3 The Two Dimensional Haar Transform A standard decomposition of a two dimensional signal (image) is easily done by first performing a one dimensional transformation on each row followed by a one dimensional transformation of each column. Construct a function that Haar transforms an image for different levels. The Haar transform is derived from the Haar matrix. The Haar transform can be thought of as a sampling process in which rows of the transform matrix act as samples of finer and finer resolution. 3. DSP IMPLEMENTATION A powerful and low price DSP is essential consideration for usage of complex computation. In market, a lot of companies provide different solutions. Texas Instrument (TI) focuses on high performance direction such as advanced video coding. Although the performance is very excellence, the price is great loading in consumer application. Fortunately, Analogy Device Company [10] provides another solution which emphasize on balance between performance and price. The Blackfin processor focuses on consumer multimedia applications in the series of DSP family of Analogy Device Company. Especially, the ADSP- BF561 is a member of the Blackfin processor which the heart of this device are two independent enhanced processor cores to offer high performance and low power consumption. The core architecture combines a dual-mac signal processing engine which belongs to signal-instruction multiple-data (SIMD) structure. The SIMD can efficient operate data in lower cycles. In power management, the Blackfin products provide dynamic management solution which can detect operation frequency and voltage to slight tune power supply. Beside another important issue is memory arrangement, hierarchical architecture such Level 1 (L1) and Level 2 (L1) as caches are implemented for low memory switch. Generally, L1 can offer zero memory waiting time is close DSP core and L2 need more cycles to move data in comparing with L1. Comparatively, L2 can own large capacities compare with L1 but lower system cycles than external memory. Due to Blackfin561 is dual core, the memory map has some different. Each core include own L1 independently, and L2 is shared memory for both. The overview of memory allocation can be depicted as follows. Level 1 memory size: 100 KB (include data and instruction) Level 2 memory size: 128 KB External memory size: can support to up to 512 MB of SDRAM by PC133-compliant controller. From above description, we select Blackfin561 as target to implement our algorithm. 3.1 Project Setup Block Diagram The block diagram defining the project setup is explained in figure 4 for implementation. The board supports video input and output applications. The ADV7179 video encoder provides up to three output channels of analog video, while the ADV7183A video decoder provides up to three input channels of analog video. The video encoder connects to the parallel peripheral interface 1 (PPI1), while the video decoder connects to the parallel peripheral interface 0, (PPI0).

Each PPI interface has an individual clock that is configured by the SW5 switch settings ( PPI Clock Select Switch (SW5)").Both the encoder and the decoder connect to the parallel peripheral interfaces (PPI input clock) of the processor. The setting for switch is done as per the specification in the manual. 3.2 Performance Parameters For the evaluation of scheme we have considered performance parameters such as Peak Signal to Noise Ratio (PSNR): Peak signal to noise ratio (PSNR) is used to evaluate the quality between the suspected watermark video frame and the original video frame. The PSNR is given by equation 3. Figure 4: Block Diagram Setup (3) (4) The MSE is given by equation 4. Where frame size is H W pixels; Cij and Rij are original frame and suspected watermarked frame pixels. Imperceptibility: The embedded watermark should not be noticeable to the viewers nor should degrade the quality of the intellectual content. It is suggested that, the PSNR value of a watermarked image should be greater than 40 db for achieving imperceptibility. Normalized Correlation (NC): The normalized correlation is utilized to evaluate the similarity between the detected watermark image with and without under attacks. It is defined as follows: NC = i j W i j ( i, [ W j ) Wˆ ( i, ( i, 2 j )] j ) (5) Where W ( i, j) = detected watermark image without under attacks and W ˆ ( i, j) = detected watermark image with under attacks. Bit Error Rate (%): Bit error rate is ratio of no of bits is error in extracted watermark to total number of bits of original watermark in percentage is given by equation 6. BER = (No. of bits in error/total no. of bits) * 100. (6) 4. WATERMARKING INSERTION AND EXTRACTION 4.1 Watermark Embedding Algorithm Figure 5 shows the proposed DWT based algorithm of embedding watermark on cover video frame. Input: Cover video frames (RGB), binary watermark, key Output: Watermarked video frames.

Step 1: In video frame preprocessing, the input video frame (RGB) of size MxN is converted into YCbCr color space. In this work, only luminance component Y is considered for watermarking. Step 2: In watermark embedding unit video frame is promoted to DWT filter. In this the watermark is added to the frames by altering the magnitude of some DWT coefficients. Only the middle frequency wavelet coefficient of the frame (middle frequency sub-band) is watermark, i.e. DWT coefficient of HL1, LH1. Figure 5: Watermark Embedding Algorithm Figure 6: Inverse Discrete Wavelet Transform (IDWT)

After watermark is embedded in the video frames in wavelet domain, Inverse Discrete Wavelet Transform (IDWT) is applied to obtain the watermarked frames and output the watermarked video is as shown in figure 6. Watermark Embedding Algorithm Flow: For each frame in video sequence, the following embedding procedure is applied. a. Convert the NxM RGB frame to YUV. b. Compute the frequency domain of the luminance layer (Y) for each frame. c. Modify the details coefficients matrices ch1[i][j] and cv1[i][j] in the middle bands in all frames. d. add pn sequences to H1 and V1 components when message = 0 e. Apply the inverse transformation to obtain the watermarked video frame Iw. Step 3: In Video frame post processing, rearrange luminance layer (Y) for each video to YCrBr(UYVY)format. 4.2 Watermark Extraction Algorithm Watermark Extraction Algorithm is shown in figure 7. Input: Watermarked video frame, key Output: Recovered watermark. Step 1: Video frame pre-processing Step 2: Watermark extraction unit The first step is to extract the piece number of the watermark from the frames. We use the secret key to find the positions of all the numbers. Due to noise, these numbers may have been distorted. Therefore, we set the piece number to be the number whose frequency is more than the threshold. Each video frame is transformed to wavelet domain with 1 level. The watermark is extracted from the frames by checking the magnitude of some DWT coefficients. Figure 7: Flowchart of Watermark Extraction Process

Decrypt the extracted watermark using the same secret key used for embedding. Watermark Extraction Algorithm Flow: There are some pieces of information stored during the embedding process such as durations of the group of frames. Based on this information, we apply the following procedure to extract the watermark. 1. Split video sequence to group of frames. 2. Apply the following algorithm in frames. a. Convert NxM RGB frames to YUV. b. Compute the frequency domain of the luminance layer (Y) for each frame. c. Extract the binary visual watermark from the middle frequency bands. 3. Combine all watermark portions, and output the watermark. 5. EXPERIMENTAL ENVIRONMENT AND RESULTS The experimentation is carried on the BF561 DSP processor. The performance results of the C algorithms are tested for test video frame Clock _322by322 which are shown in figure 8. The properties of the test video frame are highlighted in table 1. Three test binary watermarks of different sizes used for implementation of proposed algorithms are shown in figure 9. Table 2 gives test watermarks properties. All watermarks are binary image. From Table 3 we plotted a graph of number of bits of watermark image (HxW) versus PSNR(dB) of watermarked video frame / BER(%) of recovered watermark image / NC of recovered watermark image Our objective is to find a theoretical watermarking capacity (Number of Bits of watermark image) bound of digital images based on PSNR of watermarked image and NC and %BER of recovered watermark. As Number of Bits of watermark image is increased PSNR of watermarked video frame is decreased, NC is decreased and %BER of recovered watermark is increased as shown in figure 10. Figure 8: Test Video Frame Table 1: Test video properties Test video frame Type Frame size(hxw) No. of frames Frame rate Clock _322by322 AVI 322x322 12 1 frame per sec Figure 9: Test Binary Watermarks Table 2: Test Watermarks Properties Test watermark Type Image size(hxw) No. of bits of watermark image(hxw) Watermark1 BMP 9x12 108 Watermark2 BMP 11x15 165 Watermark3 BMP 21x9 189 Table 3: Performance Matrices of Proposed Algorithm for Test Video Frame for 3 Watermarks of Different Sizes Watermark Test Video frame Test watermark PSNR of NC % BER strength watermarked video frame K=1 Clock _322by322 Watermark1 40 0.940 4.6 % K=1 Clock _322by322 Watermark2 39 0.850 12.72 % K=1 Clock _322by322 Watermark3 36 0.943 13.22%

Figure 10:Plot of Number of Bits of Watermark Image (HxW) Vs PSNR(dB) / BER(%) / NC Figure 11: Result of Video In-Out utility on EZ- KIT Lite The DSP development environment is based on Visual DSP++ 4.0 IDE which is supported by Analogy Device Company. In the development flow, we design two projects on DSP system, one is watermarking insertion and another is extraction. Firstly, the insertion project will be opened and execute embedded watermarking algorithm and put embedded image on PC through JTAG. Then, the image will be tested on different condition such as a Gaussian noise, Salt & pepper noise, JPEG compression, Frame averaging, Histogram equalization, Rotation, resize & median filtering. Finally, the image will be fetched from PC and feed into DSP system by extraction project to extract watermarking.

Figure 11 shows result of executing Video In-Out utility on Analog device visual DSP++ window when PC is connected to EZ- KIT Lite board. Image viewer shows the input video frame and image configuration window shows pixel format i.e.uyvy (4:2:2). width: 858 height:525. 5.1 Video Frame Pre-Processing It is the Step 1 of watermark embedding Algorithm. The NTSC system delivers 29.97 frames/s or 59.94 fields/s. NTSC, named for the National Television System Committee, is the analog television system used in most of North America, most of South America This process of dividing frames into half-resolution fields at double the frame rate is known as interlacing as shown in Figure 12 and Figure 13. Figure 12: Luminance even (Interlaced) plan 5.2 Result of Embedding Watermark Algorithm on EZ- KIT Lite Input: 1.The result obtained with a video sequence named Shivdeepal.The sequence is composed of 200 frames (4:2:2 NTSC format) of size 704 x 480 pixel [1]. 2. Message image to hide watermark (a) (b) (c) Figure 15: (a) Original 150 th video frame, (b) 150 th Watermarked video frame and (c) Binary Watermark Imperceptibility means that the perceived quality of the video clip should not be distorted by the presence of the watermark. As a measure of the quality of a watermarked video, the Peak Signal to Noise Ratio (PSNR) is typically used. In our study, the watermark was embedded in the video according the procedure described. The PSNR of the 150 th video frame is 33 db. This high PSNR value proves imperceptibility of the proposed DWT based algorithm. 5.3 Result of Watermark extraction algorithm on EZ- KIT Lite Input: 150 th Watermarked Video frame of video sequence Output: Figure 16: Recovered watermark (Result of Water mark extraction algorithm on EZ- KIT Lite) Figure 13: Luminance odd (Interlaced) plan De-interlacing is the process of converting interlaced video, such as common analog television signals into a non-interlaced form as shown in figure 14. Table 4: Performance parameter of 150 th Water marked Video frame Parameter Values PSNR(dB): 33 NC 1 Figure 14: Luminance (De - Interlaced) plan: combined even & odd plan 5.4 Attacks in Video Watermarking Video watermarking attacks can be classified into three main groups as listed in Table 5. 1. Image processing (Non-geometric) attacks: These types of attacks attempt to damage the embedded watermark by modifications of the whole frame without any effort to identify and isolate the watermark. Examples include DCT, JPEG compression, addition of noise. 2. Geometric transformation attacks. 3. Video frame watermarking attacks.

Table 5: Types of Video Watermarking Attacks Image processing Geometric Video frame watermarking attacks (Non-geometric ) attacks transformation attacks Contrast enhancement using histogram equalization 1.Rotation 1.Frame dropping JPEG compression 2.Resize 2. Frame averaging Addition of Noise: salt & pepper noise, Gaussian noise 3.Cropping 3. Frame swapping 2D median filtering The normalized correlation is utilized to evaluate the similarity between the detected watermark image with and without under attacks. Table 6: Performance parameter of 150 th Watermarked Video frame against various attacks Sr.no. Attack type NC %BER 1 Gaussian noise 1 0 2 Salt & pepper noise 0.9880 0.925 3 JPEG Compression 0.9880 0.925 4 Frame averaging 0.9523 3.7037 5 histogram equalization 0.8095 11.3 6 Rotation 0.5595 46.296 7 Resize 0.5476 48 8 median filtering 0.5238 49 Figure 16: Attacks on 150 th Watermarked Video Frame

Figure 17: Plot of Attack Type Vs NC (Normalized Correlation) Experimental results from figure 17 shows that it is robust to common attacks in video watermarking such as geometric and non-geometric attacks, frame dropping, frame averaging, and additive noise. 6. CONSLUSION & FUTURE WORK A novel video watermarking algorithm is presented together with software and hardware consideration. The algorithm is based on Discrete wavelet transform, the wavelet perform a frequency domain operation. It has self-similarity property from wavelet; in order to fit this advantage the sub-sampling method is chosen. For watermarking extraction, the original video frame is un-useful. From the simulated robustness test results, it can be seen the performance in terms of PSNR, NC and BER the present watermarking scheme against some of commonly used attack techniques. In this project, we have proposed video watermarking scheme, including watermark preprocessing, video preprocessing, watermark embedding, and watermark detection is described in detail. It is based on DWT and the watermark is scrambled before embedding. Watermarking is done on the original video frame. As a result, the watermark is robust to a wide variety of attacks. In this project the PSNR of the watermarked video frames are an average of 38 db after undergoing all these attacks, the extracted watermark is still recognizable. Future work is to concentrate on the attacks like color gray scale conversion, cropping that has not been considered yet. The scheme can effectively resist common image processing attacks especially adding Gaussian noise, Salt & pepper noise, under contrast enhancement using histogram, JPEG compression (with quality factor of 60), frame averaging but it relatively weak under rotation, resize attack. Beside, the insertion and extraction flow are simplest, thus it is very suitable to meet the embedded system application. REFERENCES 1. F. Hartung and M. Kutter, Multimedia watermarking techniques, in Proc. IEEE, vol. 87, no. July, pp. 1079-1999. 2. R. Caldelli, M. Barni, F. Bartolini and A. Piva, A Robust Frame-Based Technique For Video Watermarking,August 1998. 3. Prof. Michael R. Lyu, Digital Video Watermarking Techniques for Secure Multimedia Creation and Delivery, Thesis for the Degree of Master of Philosophy in Computer Science and Engineering, the Chinese University of Hong Kong, July 2004. 4. Yang Gaobo, A Genetic Algorithm based Video Watermarking in the DWT Domain, 1-4244-0605-6/06/$20.00 C2006 IEEE. 5. I. Cox, M. Miller, J. Linnartz, and T. Kalker, A Review of Watermarking Principles and Practices Proceedings Digital Signal Processing for Multimedia Systems, Chapter 18, K.K. Parhi and T. Nishitani, Marcell Dekker Inc., 461-485, (1999). 6. H. Inoue, A. Miyazaki, and T. Katsura An Image Watermarking Method Based on the Wavelet Transform, Kyushu Multimedia System Research Laborator, Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference, 296-300 vol.1. 7. T. Polyák, Robust Watermarking of Video Streams,Acta Polytechnica Vol. 46 No. 4/2006. 8. Dr. Ersin ELBASI, Robust MPEG Video Watermarking In Wavelet Domain, Trakya Univ J Sci, 8(2): 87-93, 2007. 9. A Hyper-Domain Image Watermarking Method based on Macro Edge Block and Wavelet Transform for Digital Signal Processor, World Academy of Science, Engineering and Technology 43, 2008. 10. A DSP-BF561 Blackfin Processor Hardware Reference (Rev. 1.1),(2007), [Online].Available:http://www.analog.com/n/mbeddedprocessing- dsp/blackfin/processors/manuals/resources/index.html