# A Hybrid Watermarking Approach for DICOM Images Security
## 2 Related Works
[19] proposed a novel medical image watermarking method based on integer wavelet transform (IVT). This proposal verifies the integrity of ROI, precisely identifies tampered blocks inside ROI, provides robustness to the data embedded inside the region of non-interest (RONI), and recovers the original ROI. In the proposed method, the medical image is segmented into ROI and RONI regions. The hash value of ROI, recovery data of ROI, and patient data are embedded into RONI using IVT. Experimental results show that the proposed method provides robustness to the watermark data embedded inside RONI and accurately detects and locates tampered areas inside ROI and recovers the original ROI. The PSNR was equal to 40 dB and the NC was equal to 0.9. [PERSON] et al. [2] proposed a method combining a Hermite chaotic neural network and a zero-watermarking algorithm for the security of medical volume data. Firstly, it employs a novel Hermite chaotic neural network to generate the pseudo-random chaotic sequence for scrambling. Secondly, the three-dimensional medical image is transformed by a three-dimensional discrete wavelet transform and a three-dimensional discrete Fourier transform. Then, the transformed low and intermediate frequency coefficients are selected as medical volume data characteristics to structure zero-watermarking. The watermarking extraction does not need the original image, and its security depends on the chaotic sequence complexity and unpredictability, solving the watermark embedding, extraction safety, and efficiency. The simulation results show that the algorithm is simple to implement, with good robustness, security, and invisibility. The NC value was equal to 0.92. [PERSON] et al. [20] present a watermarking method that consists in concatenating patient information and image acquisition data, computing the corresponding hash using the MD5 algorithm, and embedding the result in the original image. The proposed approach is applied in the frequency domain. Four transforms were used: discrete wavelets transform, non-subsampled contourlet transform, non-subsampled shearlet transform, and discreet cosine transform. All these transforms were combined with Schur decomposition, and the watermark bits were embedded in the upper triangular matrix. Experimental results show that the proposed method is robust against several conventional attacks. The PSNR value was equal to 54 dB, and the NC value to 0.99. [PERSON] et al. [21] proposed a zero watermarking method for medical images based on Zernike-DCT. To encrypt the watermark, the authors used a chaotic logic sequence, then applied edge detection and Zernike moment processing on the original medical image to get the accurate edge points, and then discrete cosine transform (DCT) on them to get the feature vector. The perceptual hash and the zero-watermark are combined to generate the key (watermark) used in the embedding and extraction processes. Experimental results show that the NC value is 0.80, and the PSNR value is 16.3 dB. Analysis of the above methods shows that only one deals with the DICOM format commonly used for medical image transfer. Most of the proposed methods ([PERSON] et al. [4], [PERSON] et al. [18], [PERSON] et al. [19], [PERSON] et al. [2], [PERSON] et al. [20], [PERSON] et al. [21]) are implemented in the transform domain which generally leads to higher time complexity than implementations in the spatial domain and may cause difficulties in meeting time constraints related to medical diagnosis. Some of the proposed methods ([PERSON] et al. [17]) also embed the watermark in ROI and this may cause wrong diagnosis. Although the approach proposed by [PERSON] et al. [4] provides strong medical images security by combining zero-watermarking in ROI and watermarking in RONI, the extraction of the secret share is expected to be done from the RONI and only a single copy of the secret share is inserted and uses all the RONI.