# A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
## 3 Comprehensive Survey of Deep Learning-Based Image Watermarking
### Hybrid Methods
Robustness is amplified by the incorporation of perturbations during the training phase, underscoring an enhanced resilience to various forms of distortions and manipulations. [PERSON] et al. [63] introduced a hybrid methodology that intertwines traditional watermarking, CNN, and evolutionary optimization. This multifaceted approach embeds an Arnold-transformed watermark into the DCT domain, employs Harris Hawks optimization to fine-tune the embedding strength, and relies on a CNN to uncover the embedded watermark. In a separate study, [PERSON] et al. [64] presented a deep template-based image watermarking mechanism. The embedding process in their approach encodes the watermark using established techniques, employs an auxiliary locating template to manipulate a pseudo-random Gaussian noise pattern, and integrates the watermark into the red and blue color channels of a cover image. Extraction is facilitated by two deep neural networks; the initial network extracts and accentuates features, while the subsequent network classifies the watermark bit patterns. [PERSON] et al. [65] presented another nuanced technique utilizing templates for watermarking images. Their strategy involves segmenting a cover image into distinct patches, earmarking specific patches for watermark embedding, and others for housing a predefined template. Watermark insertion is executed in the curvelet domain via quantization index modulation, while the template undergoes processing by a dedicated generation network before integration into the cover image. The marked image is derived by assembling the various embedded patches. Extraction is facilitated by a template extraction network that unveils the embedded template, which is subsequently juxtaposed against the original via a template-matching network. This comparison process facilitates the identification of potential geometric distortions inflicted upon the marked image. [PERSON] et al. [66] prioritized the development of a mechanism for authenticating watermark systems via deep learning. Specifically, their framework is adept at discerning the accuracy of watermark extractions from medical images. Their innovative approach involves simulating a variety of watermark distortions and compiling a labeled dataset. This dataset then undergoes training on a neural network designed to validate the integrity of extractions derived from potentially marked images, thus bridging the gap between watermark verification and deep learning methodologies. The integration of deep learning and traditional image watermarking has given rise to a plethora of methodologies, each characterized by its distinct approach and underlying principles. Despite the diversity inherent in these hybrid methods, it is noteworthy that they tend to encounter a set of common challenges and have consequently adopted prevailing solutions to mitigate these issues effectively. These challenges largely stem from the complex interplay between the adaptive, data-driven nature of deep learning and the algorithmic, rule-based structure of traditional watermarking. Addressing these issues necessitates a nuanced approach that is sensitive to the strengths and limitations inherent in both paradigms. In Table 5, we have compiled a summary of typical challenges and their corresponding solutions.