# A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
## 4 Discussion of Potential Future Directions
Embedding watermarks in images that can be subsequently extracted from audio or video entails a complex interplay of algorithms and technologies, necessitating innovation and adaptability. One methodological prospect could involve the integration of transformer-based models, renowned for their capability to handle varied data types and complexities. Such models can be designed to embed intricate watermark patterns in images, with complementary algorithms tailored for the extraction of these patterns from audio or video formats. A synchronization protocol, ensuring the congruence of embedding and extraction processes across different media, would be integral to this approach. **Self-repair mechanisms for watermarks**. The integration of self-repair mechanisms in deep learning-based image watermarking presents an avant-garde approach to enhance the robustness and sustainability of watermarks amidst distortions or attacks. A watermark endowed with self-repair capabilities can significantly augment the reliability of information authentication and integrity verification processes. This concept aligns with the notion of regenerative embedding patterns that maintain their integrity even when subjected to complex distortions or malicious interventions. Algorithmically, this could be achieved through the incorporation of redundant encoding schemes, where the critical information is dispersed within the watermark in a manner that allows for reconstruction from partial data. Error correction codes [21] and machine learning models adept in pattern recognition and restoration [71] can be synergized to enhance the watermark's robustness. By employing neural networks trained to identify and rectify distortions, the watermark's intrinsic characteristics can be preserved. **Deep learning to extract invariant features**. Current advancements in deep learning-based watermarking leverage pre-trained neural networks, specifically honed through self-supervised learning, to bolster robustness against noise within the transformed domain. Such advancements are anchored on the premise that various self-supervised networks, especially those that employ joint feature-embedding and contrastive learning methodologies [72; 73], are effective in mitigating the effects of various types of noise. These networks ensure that multiple augmentations of a single image yield identical feature representations. Nevertheless, contemporary contrastive learning is predominantly oriented toward evaluating the representational efficacy of the learned space [74]. The metric for assessing this efficacy hinges on the network's performance in tasks encompassing classification, segmentation, and low-shot learning. Invariant feature training is an ancillary aspect, not the central focus of these learning paradigms [75]. Given this context, the direct application of pre-trained self-supervised neural networks to image watermarking can be impractical, primarily because these networks often neglect to consider ubiquitous distortions for image watermarking like perspective transformations. Consequently, there exists a notable research void warranting exploration--formulating specialized self-supervised neural networks expressly tailored for image watermarking applications. These networks would be instrumental in confronting geometric distortions, including but not limited to, rotations and perspective alterations, underscoring a pivotal frontier for ensuing inquiries. **Cover more traditional functionalities**. For deep learning-based image watermarking, a pronounced gap exists in adequately addressing traditional imperatives such as tamper detection. Contemporary methodologies predominantly concentrate on robustness, imperceptibility, and capacity, often sidelining the quintessential aspect of detecting alterations or manipulations in the watermarked images. Classical watermarking techniques have showcased efficacy in this domain, enabling the identification of unauthorized modifications with reasonable accuracy.