# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 6 Discussion, Issues and Opportunities
In this regard, the current studies have suggested some ways to increase the embedding capacity of the data based on different techniques, such as domain properties and compression algorithms [106]. The three types of image watermarking techniques were separated according to the requirements of the extraction process. All suggested solutions are either semi-blind watermarking [83], blind watermarking [23; 95], or non-blind watermarking [74; 118]. However, the non-blind watermarking methods require the original image to retrieve the hidden information, which is not suitable for certain real-time applications. In most color-image watermarking schemes, only a single watermark is embedded in the host image. These methods are ineffective in preserving both content integrity and copy protection at the same time. Unfortunately, only a few dual watermarking approaches have been proposed to address this issue [81; 104]. The watermark preparation step may include watermark scrambling using a chaotic map or Arnold transform [105; 122], which enhances robustness against cropping attacks and added noise. However, they are less helpful for attacks that alter the entire image, such as rotation, scaling, and lighting changes. In order to meet the watermarking requirements, various watermarking strategies have used optimization techniques to obtain the appropriate embedding locations or the optimal strength factor [95]. Additionally, some approaches suggested neural networks as an alternative to these optimization algorithms [113; 116]. Nevertheless, the complexity cost of these methods is high. Usually, a robust embedding space is obtained by using an invariant domain or by combining the basic domains; however, today, neural networks can learn robust watermarking domains for different situations [129]. The gain in robustness, however, is always at the expense of other watermarking requirements. Many studies focus primarily on resisting one type of attack [134], which is insufficient for certain applications. Further, some approaches focus on one watermarking requirement while ignoring the others [17]. On the other hand, most watermarking schemes are susceptible to synchronization attacks. Various attempts have been made to increase robustness against this type of attack; however, only a few schemes were tested in real-world conditions [90; 127]. Several schemes have used neural networks for embedding or extraction processes; however, they are frequently used as an additional step or complementary procedure. Some have adopted BBPN in the extraction stage to remove the noise effects, such as robustness improvements [123]. However, BPNN is inappropriate for processing huge amounts of data since BPNN depends on connections between nearby pixels, while certain studies have used neural networks to learn image fusion or concatenation capabilities as a solution to enhance robustness and imperceptibility [127; 130]. Nevertheless, this resulted in huge data losses and high computational time. Designing a deep neural network model needs a large amount of data to train. Unfortunately, even with augmentation techniques, collecting and preparing suitable datasets in large quantities is difficult, especially for real-world applications. In addition, small training datasets have a significant negative impact on model efficiency, which can be another issue for industrial applications. Usually, a learning model is trained using a particular type of dataset [136] and thus may not be applicable to another type of dataset. In addition, many researchers have used attack simulation networks to improve the robustness of the watermarking schemes [128; 133]. The original image is therefore exposed to simulated attacks during the training phase, called seen attacks, which increases the ability of the scheme to deal with these attacks. However, the trained model can withstand only a limited number of attacks, and may not be effective against unseen attacks.