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In this paper, an algorithm for multiple digital watermarking based on discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) was proposed for healthcare applications such as tele-ophthalmology, tele-medicine, tele-diagnosis, and tele-consultancy services. Multiple watermarks were used in this algorithm to reduce the consequences of medical identity thefts. In the embedding process, the cover medical image was decomposed into third-level DWT. Low-frequency bands (LH2 and LL3) were transformed by DCT, and then SVD was applied to DCT coefficients. Two watermarks in the form of images were also transformed by DCT and then SVD. The singular values of the watermark information were embedded in the singular value of the cover medical image. Watermarks were extracted using an extraction algorithm. In order to enhance the robustness performance of the image watermarks, back-propagation neural network was applied to the extracted watermarks to reduce the effects of different noise applied on the watermarked image. Results were obtained by varying the gain factor and the different cover image modalities. Experimental results were provided to illustrate that the proposed method is able to withstand a variety of signal processing attacks, and has been found to give excellent performance in terms of robustness and imperceptibility. The performance of the method was also compared with other reported techniques. Further, the visual quality of the proposed method was also evaluated by a subjective method.

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Abstract:In this paper, an algorithm for digital watermarking based on discrete wavelet transforms (DWTs) and singular value decomposition (SVD) has been proposed. In the embedding process, the host colour image is decomposed into third-level DWT. Low frequency band (LL3) is transformed by SVD. The watermark image is also transformed by SVD. The S vector of watermark information is embedded in the S component of the host image. Watermarked image is generated by inverse SVD on modified S vector and original U, V vectors followed by inverse DWT. Watermark is extracted using an extraction algorithm. In order to enhance the robustness performance of the image watermark, back propagation neural network (BPNN) is applied to the extracted watermark to reduce the effects of different noise applied on the watermarked image. Results are obtained by varying the gain factor and size of the cover and watermark image, experimental results are provided to illustrate that the proposed method is able to withstand a variety of signal processing attacks and has been found to be giving superior performance for robustness and imperceptibility compared to existing methods suggested by other authors.Keywords: image watermarking; discrete wavelet transform; DWT; singular value decomposition; SVD; back propagation neural network; BPNN; normalised correlation; NC; social applications.Reference to this paper should be made as follows: Zear, A., Singh, A.K. and Kumar, P. (2017) 'Robust watermarking technique using back propagation neural network: a security protection mechanism for social applications', Int.