# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 4 Conventional Image Watermarking Schemes
### Transform Domain
#### 4.2.1 Discrete Cosine Transform (DCT)  
DCT converts the image into equivalent frequency coefficients by modulating the frequency components. These coefficients are distributed into three regions: low, medium, and high frequency. After converting the image into the DCT domain, the left IP corner retains the majority of the image energy, which makes the DCT suitable for image compression. Moreover, it indicates a good balance between its near-optimal decorrelation and the lower complexity of the algorithm. DCT-based image watermarking methods have been the subject of many investigations. In [81], blind removal-based dual image watermarking was proposed using DCT and quantization index modulation ditm-dm. First, the watermark pattern generated from the binary watermark image is embedded into the luminance component using two secret keys, resulting in the dual watermarked image. In the extraction stage, the same two keys are employed to extract the concealed watermark. The proposed work ensures a blind removal, good image quality, and robustness to several intentional and unintentional attacks, including JPEG compression. However, using incorrect keys during the removal process will greatly distort the watermarked image. Likewise, [PERSON] et al. [82] proposed robust and secure color-image watermarking in the DCT domain for grayscale and color images. The watermark is embedded by modifying two preselected DCT mid-frequency coefficients of adjacent blocks. The Arnold transform, in addition to chaotic encryption, are used to provide security in this case. Their experimental findings revealed robustness against image-processing operations, such as compression, cropping, and median filtering. Despite this, although the discussed scheme is secure and robust against several types of attacks, including image-processing operations, it will not be suitable for some applications due to its high complexity value. In [83], a semi-blind DCT watermarking method was proposed based on differential evolution and a kernel extreme learning machine (DE KELM). Entropy is used to select non-overlapping blocks. Then, DCT is applied to the selected block. The low-frequency coefficients are selected in a zig-zag manner, considering the quality of the watermarked image. In addition, KELM used the non-linear regression model to predict the embedding coefficient for each block, while DE is used to control the watermark strength. This  
Figure 8: Watermark insertion and detection in the transform domain. watermarking scheme provides robustness to a variety of attacks, such as JPEG compression, histogram equalization, and Weiner filtering. However, it is fragile to rotational attacks. Additionally, the security and payload can be improved, and the KELM parameters can be adjusted for better imperceptibility. On the other hand, [PERSON] et al. [84] designed a watermarking scheme based on DCT for JPEG-YCbCr images. DCT is performed on the \"Y\" component, and the complexity of each generated block is computed. Then, the most complex blocks are selected for watermark embedding. In addition, both DCT coefficients and YbCr color space are stable against most changes in the host image, which increases the scheme balance. Here, Teaching-Learning-Based Optimization (TLBO) will generate the embedding parameters and the most suitable position for concealing the watermark, thus ensuring better imperceptibility of the watermarked image and high robustness against various distortions. Nevertheless, the proposed scheme is vulnerable against median filter attacks and JPEG compression. Moreover, the computational cost can be reduced. In order to achieve a good trade-off between robustness and invisibility, [PERSON] et al. [85] recommended a color-image watermarking scheme using discrete cosine transform (DCT) based on just-noticeable distortion (JND) with Arnold transform. Here, the watermark is scrambled using the Arnold transform, then embedded into the DCT coefficient of the \"Y\" channel of an image in the YbCr color representation.