## 2 Related Work  
Many methods and techniques have been developed and applied for watermarking. In this section we are mentioning some of the work developed by researchers, mainly related to our work. Singular value decomposition (SVD) and frequency domain were used in several watermarking schemes [9; 10; 11]. [PERSON] et al. [12] adopted the redundant Wavelet transform (RDWT)-SVD to obtain a singular values S which is used to embed the watermark. [PERSON] et al. [2] proposed a watermarking scheme using RDWT-SVD to determine the embedding regions with less distortion. The proposed scheme used Arnold transform to scramble the watermark image, which sequentially increased the computational cost to accomplish extra security. The author in [4] proposed the development of a Discrete Wavelet Transform (DWT) by embedding the digital watermark in the primitive image, then withdrawing the watermark from the watermarked images using Xilinx System Generator. The proposed model aims to minimize complexity, resource utilization and robustness. [PERSON] et al. [13] proposed an FrFt scheme that includes two phases for multiple image encryption, where the first phase generated keys will be used in the second phase to recover the data and keys; a contrary process, reverse FFT decryption, is used. In [6], the author proposed an improvement to the fragile watermarking using Local Binary Pattern (LBP). The proposed model uses the least significant bits technique to embed the watermarking in the cover image; it embeds 2-3 bit watermark into the rough block and 1-2 bit watermark into the smooth block. The aim of the proposed model was to use the least amount of watermark embedding to achieve a high level of image quality and protection. [PERSON] et al. in [14] proposed a semi-blind watermarking scheme for digital images using the Bat Algorithm (BA). The results show good peak signal to noise ratio (PSNR) and low normalized correlation (NC) compared to the same hybrid combination with firefly optimization. The Quick Response (QR) code was used as a watermark in [15], which proposes a blind watermarking scrambling the QR through Arnold transform before implanting the hidden information in the host signal. Different metrics were used to check the imperceptibility and to verify the robustness such as similarity index measure (SSIM), NC and Bit Error Rate. [PERSON] et al. [16] proposed a blind watermarking system using the deep learning model; two noise layers are proposed to improve the robustness. The watermark is embedded and extracted using the encoder and decoder of the model. This system is close to the PSNR watermarking method and the proposed model shows improvement in robustness, image rotation, clipping and compression. [PERSON] et al. [17] proposed a copyright protection model using a blind video watermarking algorithm, the proposed model uses MPEG-2 style to embed the watermarking information by alternating the value of the DCT coefficients during the encoding phase. Three parameters were tested: robustness, perceptibility and elapsed time for embedding the watermark. The results showed some improvement in watermarking attacks, compression attacks, noise attacks and frame-specific attacks. [PERSON] et al. [18] proposed an improvement to strengthen the LSB steganography technique by using a mask to decrease the number of changes on the image by concealing the personal information on the image, allowing an increment in the amount of data to be hidden and minimizing the distortion on the image. The authors in [19] proposed insertion of the watermark image on the edge position of the host or the original image. The proposed model identifies the edge pixel; then these pixels will be converted into binary and the logo image will be embedded and finally the image will be constructed. The proposed model used the edge detection algorithm which detects any local change in the image intensity for inserting the original image into the reference image. The PSNR and Jaccard functions are used for quality measures for images.