# A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
## 4 Discussion of Potential Future Directions
For instance, CNNs or similar deep learning architectures could be trained to discern intricate patterns and variances in visual content, enabling the model to adapt watermark placement and intensity based on different image regions. This would ensure that the watermark is not only imperceptible but also robust against various attacks, establishing a harmonious balance between visibility and security, and marking a significant stride in the advancement of image watermarking technologies. **Novel fusion methods**. Investigating innovative algorithms and techniques for the fusion of watermarks within cover images is crucial. A meticulous investigation into innovative embedding strategies is pivotal to determine a harmonious blend that ensures both the visual integrity of the cover image and the resilience of the watermark. Current methods mainly apply additive fusion and concatenation. Additive fusion integrates the watermark into the cover image by additive amalgamation, and concatenation involves the direct attachment of watermark features to the cover image. Future works can focus on embedding algorithms to ensure that watermarks are intricately woven into the cover images, balancing perceptual transparency and robustness against removal or attacks. One prospective method can be cross-attention [69], which can leverage the attention mechanism to selectively focus on specific features of the cover image during the embedding process, ensuring a dynamic and adaptive incorporation of the watermark. **Enhanced security protocols**. The imperatives of security, privacy, and integrity are being redefined by the sophistication of adversarial attacks. Consequently, the integration of innovative security protocols is not just desirable, but essential. A compelling research  
Figure 4: Summarizing and envisioning research directions in deep learning-based image watermarking. trajectory could involve the synthesis of cutting-edge cryptographic algorithms with deep learning, an amalgamation promising enhanced watermark protection. The incorporation of blockchain technology presents another frontier, offering decentralized, immutable, and transparent platforms for watermarking data transactions and validations. These multifaceted, integrative approaches are predicated on a nuanced understanding of both deep learning intricacies and the dynamics of contemporary cryptographic paradigms. As we forge ahead, the synthesis of these technologies could engender a new epoch of resilience, privacy, and security in deep learning-based image watermarking. **Efficiency and computational optimization**. The dynamic landscape of deep learning-based image watermarking is increasingly underscored by the imperative to balance computational efficiency and processing power, especially for real-time applications. There lies a complex interplay between ensuring robust watermarking and the computational load, where an optimal middle ground is sought to ensure efficiency without compromising performance. In this context, the conceptualization and development of lightweight architectures and algorithms embody a critical focal point of future research trajectories. One promising avenue involves the integration of quantization and pruning techniques [70] within the deep image watermarking models, aiming to reduce the model size while preserving the watermarking efficacy. Furthermore, the exploration of knowledge distillation could facilitate the training of compact models that inherit the performance characteristics of larger, more complex models, thereby ensuring efficiency and efficacy in tandem. **Cross-modal watermarking**. In the evolving sphere of deep learning-based image watermarking, cross-modal watermarking emerges as a frontier that offers unprecedented opportunities and challenges. It signifies the confluence of diverse media types, extending the watermarking paradigm beyond its traditional confines, and fostering a multi-dimensional approach to content protection and authentication.