# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 4 Conventional Image Watermarking Schemes
### Spatial Domain
In contrast to conventional spatial domain watermarking, the embedding procedure is carried out in [72] without significantly degrading image quality or changing perceived color. To ensure high robustness against attacks and high image quality, the watermark is spread to wider parts of the image. The current scheme conceals a logo image in the color image based on two masks, embedding mask \(M1\) and compensation mask \(M2\), where the last mask ensures that the original color distributions are least affected by the embedded bits. In addition, the use of masks ensures that the modified pixels are not noticed compared with the neighboring pixels. Results reveal the high quality of the watermarked image and its high resilience to attacks, although only the blue component is used for embedding. Having no distinction between the host image and the watermark image is the worst-case situation. Furthermore, the LSB technique is easily altered, which affects data integrity and safety. In [57] a key-based authentication scheme is described. The SHA-1 (secure hashing algorithm) algorithm is used in this case to compute the hash value for each image block, and the hash-key code is then generated and embedded in the block itself using LSBsubstitution. The watermarked image shows high PSNR and SSIM values during testing. For tamper detection, the extraction process is similar to the integration process, and the same blocks and keys are generated. At this point, the extracted bits are the hash-key code that was hidden during the embedding process. If the generated code is similar to the embedded one, it is assumed that the block has not been tampered with. The watermarking technique was able to detect different tampering shapes and sizes, and it showed resistance to various image tampering attacks, such as copy-paste, copy-move, and constant average attacks. Nevertheless, the scheme does not incorporate robustness and self-recovery features. In order to handle the computational cost, [PERSON] et al. [73] elaborated a fragile watermarking technique in the spatial domain for the tamper detection and localization of medical/general images. In this approach, the cover image has been subdivided into two blocks called upper pixel block (UHB) and lower pixel block. From the watermark data, two-bit streams were produced; one is embedded in the LHB and facilitates tamper detection, while the other is embedded in the UHB and aids in localization. The proposed scheme had low computational complexity, which makes it a better candidate for medical image authentication in real-time applications. In this case, the watermarking system is based on dividing the medical image into two regions, which are known as regions of interest. This type of watermarking is fragile, which means that it cannot withstand unintended attacks; moreover, irreversibility is the fundamental shortcoming of this work. More recently, [PERSON] et al. [74] introduced a novel digital watermarking system using a matrix multiplication-based LSB mechanism. The algorithm selects the non-consecutive matrix of the pixels to substitute the last three bits of each RGB component of the host image with the gray-scale watermark image. In order to select an appropriate block for the embedding process, the filtering process is performed on each matrix block. The opposite of the embedding procedure is used to extract the watermark. The watermark robustness is tested against several types of attacks, such as salt and pepper noise and geometric transformations. However, the proposed method is non-blind, which requires the original image at the destination side for the extraction process. Similarly, the methods used in each technique vary depending on the target application. In order to achieve the trade-off between robustness and short running time, the R matrix of QR decomposition is adopted in [75] to protect the copyright of color images in the spatial domain without true QR decomposition. Furthermore, [PERSON] et al.