텍스처 이미지가 다양한 산업 애플리케이션 분야에 널리 사용됨에 따라, 이러한 이미지들의 저작권 보호는 중요한 이슈가 되어왔다. 이러한 이유로, 본 논문은 이미지에 내재한 텍스처 특성을 이용한 칼라 텍스처 이미지 워터마킹알고리즘을 제안한다. 제안한 알고리즘은 퍼지 클러스터링을 위한 입력으로써 그레이 레벨 동시발생 행렬의 에너지와 동질성 특징을 사용하여 워터마크를 삽입하기 위한 적당한 블록들을 선택한다. 워터마크를 삽입하기 위해 먼저 선택된 블록들에 이산 웨이블릿 변환을 수행하고, 이산 웨이블릿 변환의 서버밴드들의 하나를 선택한다. 그런후에 이 워터마크를 중간 대역의 이산 코사인 변환 계수에 삽입한다. 또한, 본 논문은 워터마크 삽입 후 비인지성과 다양한 형태의 워커마킹 공격에 대해 강인성이 뛰어난 이득 계수들과 이산 웨이블릿 변환의 서버밴드들의 효과를 탐색한다. 모의실험 결과, 제안한 알고리즘은 이득 계수가 42이고 HH 밴드에 워터마크를 삽입하였을 때 높은 PSNR 값 (47.66 dB to 48.04 dB) 및 낮은 M-SVD 값 (8.84 to 15.6)을 얻었다. 또한 제안한 알고리즘은 노이즈 첨가, 필터링, 잘라내기 및 JPEG 압축과 같은 다양한 이미지 처리 공격에서도 높은 상관 값 (0.7193 to 1)을 보였다.
텍스처 이미지가 다양한 산업 애플리케이션 분야에 널리 사용됨에 따라, 이러한 이미지들의 저작권 보호는 중요한 이슈가 되어왔다. 이러한 이유로, 본 논문은 이미지에 내재한 텍스처 특성을 이용한 칼라 텍스처 이미지 워터마킹 알고리즘을 제안한다. 제안한 알고리즘은 퍼지 클러스터링을 위한 입력으로써 그레이 레벨 동시발생 행렬의 에너지와 동질성 특징을 사용하여 워터마크를 삽입하기 위한 적당한 블록들을 선택한다. 워터마크를 삽입하기 위해 먼저 선택된 블록들에 이산 웨이블릿 변환을 수행하고, 이산 웨이블릿 변환의 서버밴드들의 하나를 선택한다. 그런후에 이 워터마크를 중간 대역의 이산 코사인 변환 계수에 삽입한다. 또한, 본 논문은 워터마크 삽입 후 비인지성과 다양한 형태의 워커마킹 공격에 대해 강인성이 뛰어난 이득 계수들과 이산 웨이블릿 변환의 서버밴드들의 효과를 탐색한다. 모의실험 결과, 제안한 알고리즘은 이득 계수가 42이고 HH 밴드에 워터마크를 삽입하였을 때 높은 PSNR 값 (47.66 dB to 48.04 dB) 및 낮은 M-SVD 값 (8.84 to 15.6)을 얻었다. 또한 제안한 알고리즘은 노이즈 첨가, 필터링, 잘라내기 및 JPEG 압축과 같은 다양한 이미지 처리 공격에서도 높은 상관 값 (0.7193 to 1)을 보였다.
As texture images have become prevalent throughout a variety of industrial applications, copyright protection of these images has become important issues. For this reason, this paper proposes a color-texture image watermarking algorithm utilizing texture properties inherent in the image. The propose...
As texture images have become prevalent throughout a variety of industrial applications, copyright protection of these images has become important issues. For this reason, this paper proposes a color-texture image watermarking algorithm utilizing texture properties inherent in the image. The proposed algorithm selects suitable blocks to embed a watermark using the energy and homogeneity properties of the grey level co-occurrence matrices as inputs for the fuzzy c-means clustering algorithm. To embed the watermark, we first perform a discrete wavelet transform (DWT) on the selected blocks and choose one of DWT subbands. Then, we embed the watermark into discrete cosine transformed blocks with a gain factor. In this study, we also explore the effects of the DWT subbands and gain factors with respect to the imperceptibility and robustness against various watermarking attacks. Experimental results show that the proposed algorithm achieves higher peak signal-to-noise ratio values (47.66 dB to 48.04 dB) and lower M-SVD values (8.84 to 15.6) when we embedded a watermark into the HH band with a gain factor of 42, which means the proposed algorithm is good enough in terms of imperceptibility. In addition, the proposed algorithm guarantees robustness against various image processing attacks, such as noise addition, filtering, cropping, and JPEG compression yielding higher normalized correlation values (0.7193 to 1).
As texture images have become prevalent throughout a variety of industrial applications, copyright protection of these images has become important issues. For this reason, this paper proposes a color-texture image watermarking algorithm utilizing texture properties inherent in the image. The proposed algorithm selects suitable blocks to embed a watermark using the energy and homogeneity properties of the grey level co-occurrence matrices as inputs for the fuzzy c-means clustering algorithm. To embed the watermark, we first perform a discrete wavelet transform (DWT) on the selected blocks and choose one of DWT subbands. Then, we embed the watermark into discrete cosine transformed blocks with a gain factor. In this study, we also explore the effects of the DWT subbands and gain factors with respect to the imperceptibility and robustness against various watermarking attacks. Experimental results show that the proposed algorithm achieves higher peak signal-to-noise ratio values (47.66 dB to 48.04 dB) and lower M-SVD values (8.84 to 15.6) when we embedded a watermark into the HH band with a gain factor of 42, which means the proposed algorithm is good enough in terms of imperceptibility. In addition, the proposed algorithm guarantees robustness against various image processing attacks, such as noise addition, filtering, cropping, and JPEG compression yielding higher normalized correlation values (0.7193 to 1).
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제안 방법
Thus, the HVS has difficulty detecting a watermark embedded in complex regions. As a result, our proposed algorithm utilizes the energy and homogeneity properties of GLCMs to evaluate the complexity of regions and then chooses a suitable position to hide a watermark on the basis of these parameters. These properties are defined as follows:
Then, divide the lightness component into 8x8 blocks and compute a GLCM for each block in four directions (0o, 45o, 90o, and 135o). Finally, calculate the energy and homogeneity properties of the GLCMs.
Next, we divide the L component into several 8x8 blocks and calculate the properties of the GLCMs for all of the blocks. For a lower visibility of the embedded watermark, this paper uses FCM to classify the blocks into two categories: one category is suitable for embedding a watermark and the other is not. For this process, it is necessary to build the data set X={SubB.
Then, we embedded a watermark into DCT coefficients with a gain factor. In this study, we explored several DWT subbands and gain factors with respect to imperceptibility and robustness against various watermark attacks. Our experimental results showed that the proposed algorithm achieved higher values of PSNR, lower values of M-SVD after embedding a watermark in the HH1 band with a gain factor of 42.
For color image watermarking, it is obviously important to possess a uniform color space in order to guarantee the perceptual transparency of the color-embedded image, and the CIE LAB color space is the most suitable for this characteristic. Likewise, the HVS is less sensitive to lightness variations than hue variations [3], so the proposed algorithm utilizes the lightness component (L component in the CIE LAB color space) to embed a watermark instead of chrominance components (A and B components in the CIE LAB color space). Next, we divide the L component into several 8x8 blocks and calculate the properties of the GLCMs for all of the blocks.
Likewise, we evaluate the robustness of the proposed algorithm against various attacks such as noise addition (Gaussian noise, salt and pepper noise), Gaussian low-pass filtering, cropping, and JPEG compression. We use the normalized correlation (NC) to evaluate the robustness of the proposed algorithm as follows:
This paper proposed a color-texture image watermarking algorithm that utilizes the texture properties of GLCMs. The proposed algorithm selected suitable blocks in which to embed a watermark by using the energy and homogeneity properties of the GLCMs as inputs for the FCM clustering algorithm. To embed the watermark, we first performed one-level DWT on the selected blocks and chose one of the DWT subbands.
대상 데이터
Three different types of original images are used in this study in which each 512x512 in size with a grey level ‘ITC’ text size of 32x32 for the watermark.
이론/모형
This paper proposed a color-texture image watermarking algorithm that utilizes the texture properties of GLCMs. The proposed algorithm selected suitable blocks in which to embed a watermark by using the energy and homogeneity properties of the GLCMs as inputs for the FCM clustering algorithm.
성능/효과
In this study, we explored several DWT subbands and gain factors with respect to imperceptibility and robustness against various watermark attacks. Our experimental results showed that the proposed algorithm achieved higher values of PSNR, lower values of M-SVD after embedding a watermark in the HH1 band with a gain factor of 42. Moreover, the proposed algorithm yielded higher values of NC for all of the watermark attacks.
참고문헌 (11)
N. Wang, Y. Wang, and X. Li, "A Novel Robust Watermarking Algorithm Based on DWT and DCT," International Conference on Computational Intelligence and Security, vol. 1, pp. 437-441. 2009.
F. Kong and Y. Peng, "Color Image Watermarking Algorithm Based on HIS Color Space," 2nd International Conference on Industrial and Information Systems, vol. 2, pp. 464-467, 2010.
T. Troung and J.-M. Kim, "An Enhanced Spatial uzzy C-Means Algorithm for Image Segmentation," Journal of The Korea Society of Computer and Information, vol. 17, no. 2, pp. 49-57, 2012.
O. O. Basset, B. B. Buquet, S. S. Abouelkaram, P. Delachartre, and J. J. Culioli, "Application of Texture Image Analysis for the Classification of Bovine Meat," Food Chemistry, vol. 69, no. 4, pp. 437-445, 2000.
S.-M. Kang and J.-M. Kim, "Survey for Early Detection Techniques of Smoke and Flame using Camera Images", Journal of The Korea Society of Computer and Information, vol. 16, no. 4, pp. 43-52, 2011
Texture Analysis and Synthesis Using a Generic Markov-Gibbs Image Model, available at http://www.cs.auckland.ac.nz/-georgy/research /texture/thesis-html/node4.html.
The GLCM Tutorial, available at http://www.fp.ucalgary.ca/mhallbey/tutorial.htm.
J. C. Bezdek, J. Keller, R. Krisnapuram, and N. R. Pal, Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Springer, 1st edition, 2005.
P. U. Lande, S. N. Talbar, and G. N. Shinde, "A Fuzzy Logic Approach to Encrypted Watermarking for Still Images In Wavelet Domain of FPGA," Int'l J. Sig. Proc., Image Proc., and Patt. Recog., vol. 3, no. 2, pp. 1-10, 2010.
H. A. Abdallah and M. M. Hadhoud, "Blind Wavelet-Based Image Watermarking," Int'l J. Sig. Proc, Image Proc., and Patt. Recog., vol. 4, no. 1, pp. 15-28, 2011.
S. Kishk, H. E. M. Ahmed, and H. Helmy, "Integral Images Compression Using Discrete Wavelets and PCA," Int'l J. Sig. Proc., Image Proc., and Patt. Recog., vol. 4, no. 2, pp. 65-78, 2011.
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