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A Robust Evolutionary Based Digital Image Watermarking Technique in DCT. Domain. Majid Rafigh. Electrical and Computer Engineering Department.

A Robust Evolutionary Based Digital Image Watermarking Technique in DCT Domain Majid Rafigh

Mohsen Ebrahimi Moghaddam

Electrical and Computer Engineering Department Shahid Beheshti University Tehran, Iran [email protected]

Electrical and Computer Engineering Department Shahid Beheshti University Tehran, Iran [email protected]

Abstract— Digital watermarking techniques are used for the digital right managements and copyright protection. As known, it is a big deal in watermarking systems to make a good trade of between the robustness and imperceptibility. This paper presents a watermarking algorithm in the DCT domain using an evolutionary algorithm to satisfy both of robustness and imperceptibility. We employ a genetic-based algorithm to select pairs in DCT coefficients and insert watermark bit according to mathematical relation between selected coefficients in each 8×8 DCT block of image. The proposed method has been implemented and tested under various attacks including JPEG compression, additive noise distortion, and image filtering. The achieved results show that image remains imperceptible while the watermark survives the attacks especially in case of JPEG compression. Keywords-component; Copyright protection, Algorithms, Discrete Cosine Transform (DCT)

I.

Genetic

INTRODUCTION

Watermarking technique is used to protect multimedia by embedding the ownership information into content with slight modifying. This ownership information can be extracted and used for authentication and ownership protection [1]. Robust watermarks are designed to be detectable after some image processing operations called attacks [2]. Watermark insertion can be done in either spatial domain or frequency domain. The spatial-domain watermark insertion manipulates image pixels. However, the spatial-domain watermark insertion is simple and easy to implement, it is weak versus various attacks and noise [3]. On the other hand, the frequency-domain watermark insertion that is based on the frequency coefficients processing is more robust against attacks [3]. There are in literature several watermarking techniques in different frequency domain such as Discrete Fourier Transform (DFT) [4], Discrete Cosine Transform (DCT) [5] [6], and Discrete Wavelet Transform (DWT) [2] [7]. For example, in [5], Yen chose pairs of positions with same quantization scale in standard JPEG quantization table as host image candidates for embedding the watermark information. The reason for selection these candidates are that all DCT coefficients are divided by the same value in quantization table at JPEG compression process, therefore, relationship between two DCT coefficients of the same coordinate position from two blocks are not changed after

quantization process [8] and this makes the method robust versus jpeg compression. Also, an estimation-based watermarking technique, based on the estimation of DCT AC coefficients has presented in [9]. In another approach, some watermarking techniques have employed GA (Genetic Algorithm). For example, Lee et.al [3] has inserted watermark to the low-frequency region of wavelet transform domain. They used a genetic algorithm-based method for watermark extraction. In another work, Shieha [2] applied genetic algorithm to find the optimal frequency bands for watermark embedding into DCT domain. In the present paper, we proposed a blind watermarking algorithm that uses DCT coefficients to embed watermark in the image. The algorithm adopts GA to find the best position of DCT domains for watermark insertion. To achieve a robust watermark embedding against common attacks such as Jpeg compression, low pass filtering, and noise; proposed algorithm uses mathematical relationships between DCT coefficients. The simulation results showed that watermark insertion by proposed method made images with acceptable PSNR and it was robust versus attacks. The rest of paper has been organized as follows. In Section 2, we present the proposed watermarking scheme including the embedding and extracting processes in detail. In Section 3, the experimental results are presented to show the performance of proposed scheme and finally section 4 concludes the paper. II.

THE PROPOSED SCHEME

A. Embeding Algorithm The position of watermarking plays an important role when the tradeoff between robustness and invisibility is considered. High frequency positions will be lost in most common image processing attacks and low frequency band has visual effects, therefore, a tradeoff should be considered In the proposed algorithm, at first, the image is divided to 8*8 blocks and each block is transformed to DCT domain. For embedding the watermark, two DCT coefficients that their difference is big enough and they are in low frequencies are chosen. Greater amount of this distance guarantees more robustness of algorithm.

To find the best coefficients for watermark insertion, a genetic algorithm has used in each 8×8 DCT block. Initially, some chromosomes (parents) are generated randomly. Each chromosome is a pair of index of zigzag ordered of a DCT block. Figure 1 shows the numbering order of DCT coefficients based on Zigzag transform. The fitness of the chromosomes are measured according to Equation 1 where x and y are DCT coefficient index and bx and by are their corresponding value. α and β are parameters to balance the fitness and are determined by excrements.

d. e.

for blocki. This vector will be used in watermark extraction process as secret key. Apply inverse DCT on each block. Combine all blocks to acquire the watermarked image.

Figure 1: Zigzag ordered DCT block |

| ∆ |

,

1

,

0

(1)

|



As it is shown in equation 1, the fitness function has two parts. First part deal with distance between selected coefficients and the second part controls that selected positions are in low frequency band. Also, value of Δ is a predefined constant and used as a reference threshold. Greater value of Δ provides more robustness but it has negative effect on quality of watermarked image. The chromosomes with best fitness are selected and used for the next generation. The whole procedure of watermark embedding is outlined in Figure 2. This scheme is described step by step as follows: 1. Image Blocking: divide the input image of size M×N into 8×8 non over-lapping blocks. 2. Apply the following steps to all blocks: a. Transform the block to DCT domain. b. Use genetic algorithm to find best positions to embed wi into bi , where wi is ith bit of watermark and bi is ith block. c. Modify DCT coefficients to satisfy Equation 2: 1 0

| |

∆ | |



Figure 2: Watermark embedding block diagram B. Extraction Algorithm In extracting the watermark message, the original image is not required in our algorithm. Extraction algorithm uses the positions that found with GA in embedding phase as extraction key. According to the key, with Equation 3 the embedded watermark can be extracted. 1, 0,

| |

Present the extracted watermark bit from DCT block i.

(2)

x and y are best positions that has best fitness in genetic algorithm and Δ is a predefined value. Best positions will be stored in a vector as keyi

(3)

Figure 3: Watermark extraction block diagram

Block diagram of the watermark extracting process is illustrated in Figure 3. EXPREMINTAL RESULTS

This section demonstrates the experimental results of the proposed algorithm. To evaluate the performance of the proposed scheme, some experiments were conducted on various standard 8 bit gray scale images of size 512×512 pixels by different kinds of attempting attacks. A random binary sequence with size 4096 was generated as watermark message. The initial population size consisted of 20 chromosomes. The number of generations was considered as 30 times that was identified in experiments. The best gene is considered as watermark embedding position. Table 1 represents the parameters used in the experiments for genetic algorithm. The probability of crossover was empirically set to 0.95%, and mutation rate was 0.05%. We used single point crossover between two chromosomes to make new gens. The mutation function changes one of indexes in chromosome with a random index. Then, chromosomes which have higher fitness have been selected for new population set. In fitness equation (Equation 1) parameter β depends on maximum value of where x and y are indexes in zigzag ordered DCT block. In this case we set β to 128. Parameter α describes the amount of Δ impact on fitness. Base on the simulation results, the best value of α considered as 2 and Δ equal to 20. Table 1: Genetic Algorithm Parameters

Parameters

Values

Population size Maximum number of generation Selection rate Crossover rate Mutation rate Δ

20 30 0.90 0.90 0.05 20

Figure 4 and Figure 5 show the effect of number of generations and value of Δ on watermark image quality when watermark embedding process applied on Lena image with different generations and Δ values. If selected position by GA algorithm did not satisfy Equation 2 then the coefficients of selected positions should be modified. The percent of blocks that have been modified after finding the best positions by GA for embedding a 4096 bit watermark in Lena image 512×512 have been shown in Figure 4.

Modification Percent

III.

35

Δ=10 Δ=20

30 25 20 15 10 5 0

0

1

5 10 15 20 25 30 35 40 Number of generations

Figure 4: Percent of blocks that needs modification for Lena image 512×512 pixels. To compare the performance of our scheme results, the peak signal-to-noise ratio (PSNR) value for imperceptibility, error rate and normalized correlation (NC) value were used. Equation 4 shows the PSNR of an image: 10

(4) ∑



,

,

(5)

In these equations, p[i, j] denotes the component values of the pixel [i, j] in the original and the embedded watermark images, respectively. The larger PSNR, the better the image quality will be. In general, an embedded watermark image is acceptable by human perception if its PSNR is greater than 30 dB [10]. Equation 6 shows the normalized correlation value: ∑ ∑

(6)

Where w represents the inserted watermark and w´ shows the extracted watermark. This section also compares the effect of Δ on PSNR. Figure 5 shows the variation of the PSNR of watermark embedded image versus different GA generations and values of Δ. This figure shows that higher value for Δ caused more destruction on image and PSNR improved when the generation of GA increased.

PSNR

Table 4: Successful watermark extraction under different attacks (NC)

70 60 50 40 30 20 10 0

Δ=10 Δ=20 0

Attack category

NC(Yen)

NC(Wang)

NC(ours)

Salt & Pepper Gaussian Noise Median Filter Mean Filter Sharpening Filter

0.97 0.96 0.97 0.94 0.96

0.95 0.96 0.97 0.97 0.98

0.97 0.95 0.99 0.96 0.98

IV.

1

5 10 15 20 25 30 35 40 Number of Generations Figure 5: PSNR of watermarked Lena image 512×512 with 4096 bit watermark vs different GA generations Also, the results of proposed method were compared with two other DCT based approaches; Yen’s method [5] and Wang’s method [9]. To compare watermark imperceptibility among different techniques, the PSNR values of different methods on different images are shown in Table 2. As it is shown in this table, the proposed scheme obviously has the higher values of PSNR versus other methods. Table 2: Imperceptibility using PSNR (db) Image

Yen’s method

Wang’s method

Proposed method

Lena Baboon Crowd

48.09 44.98 46.86

39.02 37.08 32.79

49.74 49.16 48.94

Table 3 lists simulations results when the images have been corrupted by JPEG compression. Different quality factors were applied to watermark embedded of Lena 512×512 image and extraction algorithm was used to get watermark message. As shown, when the quality factor is low, our method provides more robust watermark extraction. Table 3: Watermark extraction robustness against JPEG compression (for gray scale Lena 512×512) Quality Factor

NC(Yen)

NC(Wang)

NC(ours)

70 50 30 10 5

0.99 0.96 0.51 0.49 0.46

0.98 0.81 0.63 0.48 0.43

1.0 1.0 0.99 0.75 0.66

Table 4 demonstrates the results of proposed method precision versus several common image processing attacks. The success rates of watermark extraction are listed. Attacks parameters change due to the simulation and the average values are shown in table.

CONCLUTIONS

In this paper, we proposed a novel and effective robust watermarking approach based on genetic algorithm. The major contributions of proposed method is using genetic algorithm to find best positions in image blocks that the positions have a mathematical relationship. We used these positions to watermark embedding and retrieval in DCT domain. The proposed method is robust against a wide variety of tests as indicated in the experimental results. In particular, it is more robust against JPEG compression. V.

REFRENCES

[1] Lian, Shiguo, Kanellopoulos, Dimitris and Ruffo, Giancarlo., "Recent Advances in Multimedia Information System Security." Informatica, s.l. : Informatica, 2009, Vol. 33. [2] Shieha, Chin Shiuh, Huang, Hsiang Cheh and Hsin, Feng., "Genetic watermarking based on transform-domain techniques." s.l. : Pattern Recognition, 2004, Vol. 37. [3] Lee, Dongeun, et al., "Genetic Algorithm-Based Watermarking in Discrete Wavelet Transform Domain." Kunming , CHINE : International Conference on Intelligent Computing, 2006. [4] Qi, Xiaojun and Qi, Ji., "A robust content-based digital image water marking scheme." s.l. : Signal Processing, 2007, Vol. 87. [5] Yen, Shwu Huey, et al., "A watermarking technique based on JPEG quantization table." Taipei, Taiwan : Proceedings of the 2006 International Computer Symposium (ICS2006), 2006. [6] Al-Gindy, Ahmed N, et al., "A New Blind Image Watermarking Technique for Dual Watermarks Using Low-Frequency Band DCT Coefficients." 2007. [7] Agrestea, Santa, Andalorob, Guido and Prestipin, Daniela., "An image adaptive, wavelet-based watermarking of digital images." s.l. : Journal of Computational and Applied Mathematics, 2006.

[8] Rey, Christian and Dugelay, Jean Luc., "A Survey of Watermarking Algorithms for Image Authentication." s.l. : EURASIP Journal on Applied Signal Processing, 2002, Vol. 6. [9] Wang, Yulin and Pearmain, Alan., "Blind image data hiding based on self reference." s.l. : Pattern Recognition Letters, 2004, Vol. 25. [10] Chen, T S, Chang, C C and Hwang, M S., "A virtual image cryptosystem based upon vector quantization." s.l. : IEEE Transactions on image processing, 1998, Issue 10, Vol. 7.

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