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Machine learning based blind color image watermarking scheme for copyright protection

机译:基于机器学习的盲彩图像水印方案,用于版权保护

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This work presents a blind and robust scheme using YCbCr color space, IWT (integer wavelet transform) and DCT (discrete cosine transform) for color image watermarking. During watermark insertion, Y channel is divided into blocks and Mersenne Twister random number generator is used to select the blocks for embedding. This randomized selection of blocks required a secret key, thus improving the security of the scheme. To reduce the computational complexity, the artificial neural network architecture is developed for watermark embedding. To check the robustness, several signal processing attacks such as JPEG compression, filtering attacks, noise attacks, cropping, resizing and other common attacks are applied on the watermarked images. The proposed work is tested on different images to verify the similarity in watermarking results. The scheme provides similar results (having little variation) for different test images. Experimental results demonstrate the superior performance in terms of imperceptibility and robustness. Further, the ANN framework provides faster embedding with approximately similar parametric results. The performance comparison with existing schemes demonstrates better performance for different attacks. The proposed work can be used in robust applications (i.e. copyright protection) for efficient results and less computational time.(c) 2021 Elsevier B.V. All rights reserved.
机译:这项工作介绍了使用YCBCR颜色空间,IWT(整数小波变换)和DCT(离散余弦变换)进行彩色图像水印的盲和鲁棒方案。在水印插入期间,Y通道被分成块,Mersenne Twister随机数发生器用于选择用于嵌入的块。这种随机选择的块需要一个秘密密钥,从而提高了方案的安全性。为了降低计算复杂性,开发了人工神经网络架构用于水印嵌入。为了检查稳健性,在水印图像上应用了诸如JPEG压缩,过滤攻击,噪声攻击,裁剪,调整大小和其他常见攻击的多个信号处理攻击。在不同的图像上测试所提出的工作,以验证水印结果中的相似性。该方案提供了不同的测试图像的类似结果(几乎没有变化)。实验结果表明了难以察觉和鲁棒性方面的卓越性能。此外,ANN框架提供了更快的嵌入嵌入,具有大致相似的参数结果。与现有方案的性能比较展示了不同攻击的更好性能。拟议的工作可用于有效的应用程序(即版权保护)以获得有效的结果和较少的计算时间。(c)2021 Elsevier B.v.保留所有权利。

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