# A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
## 1 Introduction  
Each year, the internet serves as a conduit for the upload, transfer, and sharing of billions of digital images [1]. The advent of sophisticated digital technologies has facilitated the effortless editing, dissemination, and reproduction of images, precipitating a surge in unauthorized usage and concomitant infringement of the intellectual property rights of original creators. In this context, digital image protection emerges as a critical mechanism to enforce the sanctity of the intellectual property of content creators. Digital images are not merely visual content; they are significant assets for individuals, corporate entities, and various organizations. The integrity of these assets is often threatened by unauthorized utilization and replication, a scenario that could culminate in substantial financial deficits and damage to reputation. Moreover, the illicit use of personal images can inflict emotional distress, exacerbated when images or videos are circulated without the owner's consent. Digital image watermarking has emerged as a preeminent technique in the domain of image protection, garnering widespread application and acclaim [2]. Central to this technique is the covert embedding of informational elements, ranging from logos to copyright notices, directly into the visual content. This surreptitious integration ensures that only individuals endowed with the appropriate authorization can extract the watermark, maintaining the confidentiality and integrity of the embedded data [3]. Watermarks serve a multifaceted purpose. They act as an indelible signature, affirming ownership and bolstering copyright protection by dissuading unauthorized replication and distribution. They stand as testaments to authenticity, facilitating the licensing and tracking of image utilization. Moreover, they serve as conduits for covert communication, encapsulating hidden messages that are seamlessly woven into the visual content. Lastly, they function asintrinsic detectors, revealing alterations and tampering, thereby upholding the integrity of the original content. The versatility of digital watermarking extends its applicability across a plethora of fields. It has become an instrumental tool in forensic analyses, enhancing the traceability and verification of digital content. In the burgeoning spheres of 5G communication and the Internet of Things (IoT) [4], watermarking is pivotal in fortifying security and enhancing data integrity. Smart cities, characterized by their intricate networks of interconnected digital systems, leverage watermarking to safeguard data and ensure the seamless, secure interchange of information [5]. Image watermarking and image steganography [6] are closely related fields, yet with distinct technical and application-specific differences. Both areas explore the intricate process of subtly embedding data within images, ensuring the modifications remain unnoticeable to the unaided eye. However, the differing goals they aim to accomplish lead to distinct technical focuses. Image steganography primarily aims to provide a covert channel for information transmission, avoiding detection by unauthorized parties. It hinges on the principles of unpredictability and high payload capacity [7; 8]. The former emphasizes resistance against steganalysis techniques, while the latter denotes the ability to embed a significant amount of data without affecting the perceptual quality of the cover image. On the other hand, image watermarking seeks to protect the integrity of both the cover image and the embedded watermark. The fundamental aspect of this field is robustness, which refers to the enduring readability of the watermark amidst various potential attacks. This characteristic is crucial, although there are situations where fragile watermarking is preferable and necessary, especially in scenarios like medical imaging, where maintaining the original quality of the image is vital [9]. Traditional image watermarking approaches are predominantly characterized by handcrafted embedding and extraction mechanisms.