# A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
## 4 Discussion of Potential Future Directions  
The proliferation of proposals concerning deep learning-based image watermarking has instigated our comprehensive survey aimed at bridging historical, contemporary, and prospective research. Figure 4 encapsulates the prevailing trends delineated in Section 3 and extrapolates future investigative trajectories. The contemporary focus gravitates toward the intricacies of noise layer differentiation, diversity in noise types, enhancements in architecture and training paradigms, and the strategic integration of deep learning within image watermarking. This survey underscores a spectrum of untapped research avenues that transcend traditional frameworks. These emergent perspectives are poised to foster considerable innovations in this domain, skillfully navigating the complex interplay of robustness, imperceptibility, capacity, and security requisite in the dynamic realm of digital media and communications. The remainder of this section discusses our proposals for potential research directions for the future. **Robustness toward unforeseen noise. ** Deep learning-based image watermarking models exhibit distinct robustness to various types of noise, a characteristic intricately linked to the specific noise types they are trained on. This variance in robustness is prominently observed when contrasting the model's performance against trained and untrained noise types. Trained noise types refer to those the model has been explicitly exposed to during the training phase, allowing it to develop specialized mechanisms to counteract their effects. Consequently, the model's efficacy in watermark extraction remains largely stable when encountered with these familiar noise types. Conversely, untrained noise types introduce an element of unpredictability. Since the model lacks prior exposure and adaptive development against these noise types, its performance can potentially be \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Challenges** & **Representative Solutions** \\ \hline Determining the optimal role of deep learning in hybrid watermarking frameworks & Employing deep learning to enhance watermark extraction processes \\ \hline The integration of deep learning and traditional watermarking techniques often results in augmented complexity & Crafting modular and scalable architectures facilitate seamless integration and interoperability between both methodologies \\ \hline Refining the synergy between embedding and extraction processes is essential, given the distinct strengths and weaknesses inherent to each approach & Utilizing deep learning for its adaptability and learning prowess, complemented by leveraging the proven properties of traditional algorithms \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the challenges and representative solutions in hybrid methods. compromised. This differential in robustness underscores the critical importance of a comprehensive training regimen that encompasses a diverse array of noise types to bolster the model's generalization capabilities. Future research could focus on enhancing model adaptability and robustness against untrained noise types, perhaps through the integration of online learning methods [67], or meta-learning [68] strategies that equip the model to swiftly acclimatize to unfamiliar noise environments. **Content-aware watermark embedding**. A predominant focus of deep learning-based image watermarking has been accorded to static images. This static orientation potentially undermines the watermark's efficacy, given that optimal embedding strategies can significantly vary across different content types and dynamic scenarios. A transition toward content-aware watermark embedding techniques has the potential to redress this imbalance. This approach, conceptualized to be inherently adaptive, is envisaged to utilize sophisticated algorithms capable of analysis and adaptation to the unique attributes of each image or media sequence.