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A proposed secure multiple watermarking technique based on DWT, DCT and SVD for application in medicine

机译:一种基于DWT,DCT和SVD的安全多重水印技术在医学中的应用

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摘要

In this paper, an algorithm for multiple watermarking based on discrete wavelet transforms (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD) has been proposed for healthcare applications. For identity authentication purpose, the proposed method uses three watermarks in the form of medical Lump image watermark, the doctor signature/identification code and diagnostic information of the patient as the text watermarks. In order to improve the robustness performance of the image watermark, Back Propagation Neural Network (BPNN) is applied to the extracted image watermark to reduce the noise effects on the watermarked image. The security of the image watermark is also enhanced by using Arnold transform before embedding into the cover. Further, the symptom and signature text watermarks are also encoded by lossless arithmetic compression technique and Hamming error correction code respectively. The compressed and encoded text watermark is then embedded into the cover image. Experimental results are obtained by varying the gain factor, different sizes of text watermarks and the different cover image modalities. The results are provided to illustrate that the proposed method is able to withstand a different of signal processing attacks and has been found to be giving excellent performance for robustness, imperceptibility, capacity and security simultaneously. The robustness performance of the method is also compared with other reported techniques. Finally, the visual quality of the watermarked image is evaluated by the subjective method also. This shows that the visual quality of the watermarked images is acceptable for diagnosis at different gain factors. Therefore the proposed method may find potential application in prevention of patient identity theft in healthcare applications.
机译:本文提出了一种基于离散小波变换(DWT),离散余弦变换(DCT)和奇异值分解(SVD)的多重水印算法。为了身份认证目的,所提出的方法使用医疗块图像水印,医生签名/识别码和患者的诊断信息形式的三个水印作为文本水印。为了提高图像水印的鲁棒性,将BP神经网络应用于提取的图像水印,以减少噪声对水印图像的影响。通过在嵌入封面之前使用Arnold变换,还可以增强图像水印的安全性。此外,症状和签名文本水印也分别通过无损算术压缩技术和汉明纠错码进行编码。然后将经过压缩和编码的文本水印嵌入到封面图像中。通过改变增益因子,不同大小的文本水印和不同的封面图像模态,可以获得实验结果。提供的结果表明,该方法能够抵御不同的信号处理攻击,并被发现在鲁棒性,不可感知性,容量和安全性方面均具有出色的性能。还将该方法的鲁棒性与其他报道的技术进行了比较。最后,还通过主观方法来评估水印图像的视觉质量。这表明水印图像的视觉质量对于不同增益因子的诊断是可以接受的。因此,所提出的方法可以在预防医疗保健应用中的患者身份盗窃中找到潜在的应用。

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