# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 1 Introduction
There are some problems where traditional techniques with global features are a better solution. The advent of DL can pave the way for complex problems to be solved, or it can be combined with traditional techniques to fill in gaps and achieve better results [22; 23]. To date, we have covered several concepts that are generally interconnected. However, it remains unclear how deep learning has been integrated into the digital image watermarking, what contri  
Figure 2: Recent applications of watermarking. bution it has made to the field, and whether deep learning can really replace traditional techniques-based watermarking. Several surveys on watermarking methods were proposed in the literature [7; 21; 24; 25]. To the best of our knowledge, no work has been performed to investigate the transition of image watermarking from traditional techniques to ones based on deep learning. In this paper, we intend to explore current trends in digital image watermarking techniques. We will discuss two aspects of image watermarking. The first includes traditional techniques, and the second depends on deep learning architectures. We believe this classification will be useful in understanding the transition from traditional methods to deep learning-based methods. Alternatively, by comparing image watermarking schemes collected in the upcoming summary tables, researchers could gain significant insights into the image watermarking field with its new (modern) and old (traditional) technologies. Figure 3, shows the number of documents (conference papers, articles, reviews, book chapters, etc.) by year according to Scopus data, using watermarking as a keyword in Figure 2(a) and watermarking-based learning in Figure 2(b). As can be seen, after a period of stagnation between 1990 and 1995, the number of watermarking publications increased and reached a peak in 2010. On the contrary, watermarking-based learning publications began at the end of the 1990s, fluctuated between 2000 and 2015, and have since increased. In this review paper, the selection of references was performed as follows: Before 2015, the selection was based on a compromise between the paper's popularity (citations, journals), and its easy readability using classical keywords (watermarking, steganography, data hiding, etc.). After 2015, the selection was based on the popularity and variability of the proposed ideas and novelties based on additional keywords (machine/deep learning and watermarking). The following are the important contributions of this work: (1) To begin, we introduce a comprehensive taxonomy of digital image watermarking schemes based on several concepts and criteria. (2) Then, we present a recent state-of-the-art watermarking approach based on working domains, and a summary of the research results is described in tabular format. (3) Afterwards, we review the deep learning-based image watermarking methods and summarize and compare the contributions of the reviewed approaches in different technical perspectives. (4) Subsequently, we classify most popular reviews from the earlier sections according to how resilient they are to different forms of attacks. This classification aims at providing a clear vision of the strengths and weaknesses of deep learning technologies compared with traditional ones. (5) Finally, we state the main issues and challenges, as well as potential future directions. This paper is organized as follows: Section 2 describes background concepts about digital image watermarking, including watermarking phases and different type of attacks. Section 3 presents digital image watermarking requirements with objectives measurements used to evaluate watermarking schemes. Section 4 reviews related algorithms on image watermarking. Furthermore, we discuss the current state-of-the-art image watermarking-based traditional techniques. Our reviewed techniques are also summarized in this section. Section 5 presents a comprehensive review and summary of modern image watermarking  
Figure 3: Documents per year based on Scopus website.