Image watermarking is an effective way to secure the ownership of digital photographs. This paper proposes a new methodology for integrating a watermark on the basis of various integrative strengths. The image is separated as 8 × 8 pixels blocks that do not overlap. The pixel size for each image block has been determined. For the embedding areas, picture blocks with the highest value have been chosen. Therefore, discrete cosine transformation (DCT) is transformed. The DCT coefficients are chosen in the midfrequency and the average selected DCT blocks are determined using a series of rules to produce various integration strengths. The watermarking bits were merged with the proposed deep learning convolution neural network (DLCNN) through a series of integration standards. The binary watermark has been scrambled by an Arnold transform until it is incorporated for additional stability. During the image carrier, a pattern recognition model depending on DLCNN is utilized to identify and extract the watermark and to recognize the watermark using the Harris hawks optimization (HHO) algorithm. The findings of the tests demonstrated that the system suggested is most imperceptible than the other current systems. The proposed method attains the efficiency watermarked picture with 46 dB peak signal-to-noise ratio value. This paper focuses on robust medical image watermarking exploiting DCT by using the HHO algorithm. The watermark lossless compression reduces watermark payload without data loss. In this research work, watermark is the consolidation of DCT and image watermarking secret key. The performance of robust medical image watermarking exploiting DCT with the HHO algorithm is compared with other conventional compression methods. HHO is found better and used to control watermarked image degradation in medical images watermarking. The proposed system also created a high resistance to remove watermarks during many attacks.

中文翻译：

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利用 DCT 和 Harris hawks 优化的基于深度学习的鲁棒医学图像水印

图像水印是保护数码照片所有权的有效方法。本文提出了一种基于各种集成强度的水印集成新方法。图像被分隔为不重叠的 8 × 8 像素块。已确定每个图像块的像素大小。对于嵌入区域，选择了具有最高值的图片块。因此，离散余弦变换（DCT）被变换。在中频中选择 DCT 系数，并使用一系列规则确定平均选择的 DCT 块，以产生各种集成强度。水印位通过一系列集成标准与提出的深度学习卷积神经网络（DLCNN）合并。二进制水印已被 Arnold 变换加扰，直到它被合并以增加稳定性。在图像承载过程中，利用基于DLCNN的模式识别模型来识别和提取水印，并使用Harris hawks优化（HHO）算法识别水印。测试结果表明，建议的系统比其他当前系统最不易察觉。所提出的方法获得了具有46 dB峰值信噪比值的高效水印图像。本文重点研究利用HHO算法利用DCT的鲁棒医学图像水印。水印无损压缩在不丢失数据的情况下减少了水印有效载荷。在这项研究工作中，水印是DCT和图像水印密钥的结合。将使用HHO算法的DCT鲁棒医学图像水印的性能与其他传统压缩方法进行了比较。在医学图像水印中，HHO 被更好地用于控制水印图像的退化。所提出的系统还在许多攻击中创建了去除水印的高抵抗力。