# A Robust and Secure Watermarking Approach Based on Hermite Transform and SVD-DCT
## 2 Related Work
Moreover, the authors introduced a combined watermarking technique that leveraged the advantages of multiple spatial-frequency decomposition approaches such as the DWT and DCT, robust insertion analysis such as SVD, and security such as the AT. [PERSON] et al. [20] used a cryptographic technique called Elliptic Curve Cryptography (ECC) in a semi-blind strategy of digital image watermarking. The proposed watermarking method was implemented within the DWT and SVD domain. Furthermore, the parameters of the entropy based on the HVS were calculated on a blockwise basis to determine the most-appropriate spatial locations. [PERSON] et al. [21] presented a spectral domain watermarking technique that utilized QR codes and QIM in the YCbCr color domain, and the luminance channel underwent processing through SVD, the DWT, and the DCT to insert a binary watermark using QIM. In [22], [PERSON] et al. presented two hybrid watermarking schemes for securing 3D video transmission. The first one was based on the SVD in the DWT domain, and the second scheme was based on the three-level discrete stationary wavelet transform in the DCT domain. In addition, [PERSON] et al. [23] proposed a fusion technique utilizing wavelets to combine two depth watermark frames into a unified one. The resulting fused watermark was subsequently secured using a chaotic Bakermap before being embedded in the color frames of 3D-High-Efficiency Video Coding (HEVC). [PERSON] et al. [24] introduced a robust and imperceptible watermarking technique for RGB images in the combined DWT-DCT-SVD domain. Initially, the luminance component undergoes decomposition using DWT and DCT. The feature matrix is generated by extracting the low and middle frequencies of the DCT from each region, which is subsequently subjected to SVD for watermark embedding. In [25], [PERSON] et al. reported an image watermarking algorithm using hybrid transforms. In this approach, using SVD analysis, the decomposition of the image watermark was embedded in the decomposition of the cover image using the Normalized Block Processing (NBP) to obtain the invariant features. Then, the integer wavelet transform was applied, followed by the DCT and SVD. In [26], [PERSON] et al. provided an overview of the watermarking techniques used in medical image security. The authors described the elements to design a watermarking algorithm. Furthermore, they presented a brief explanation of cryptography, steganography, and watermarking. Regarding watermarking, they took as an example different algorithms such as that in [27], where [PERSON] et al. presented a watermarking algorithm to ensure the copyright protection of medical images. They used as the watermark patient information and used the DWT. The results showed high PSNR values (147 dB), demonstrating the imperceptibility of the watermark and the robustness of the method against attacks. However, they did not present any results about the extraction process. In the paper [28], [PERSON] et al. described a watermarking algorithm using thirty different images and used two watermarks: one of them to authenticate (fragile), and the other one focused on robustness (information watermark). To insert the authentication watermark, they used the DCT, and for the information watermark, the process included the DWT and SVD. The results showed robustness for Salt and Pepper (SP) noise, rotation, translation, and cropping (even though the PSNR of the recovered watermark was low). The same authors proposed another watermark algorithm in [29]. This algorithm was non-blind and used the LWT on the cover image to decompose the image into four coefficient matrices; with this transform, the image had better reconstruction. Furthermore, the authors employed the DCT and SVD. The authors reported better robustness and mentioned that they reduced the time complexity of traditional watermarking techniques. The results showed high PSNR values (about 200 dB) without attacks.