# A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
## 3 Comprehensive Survey of Deep Learning-Based Image Watermarking
### Hybrid Methods  
Methods encompassed in this category exhibit a fusion of deep learning techniques and traditional calculations associated with image watermarking. Such an integration implies a symbiotic relationship where the strengths of one approach compensate for the weaknesses \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Challenges** & **Representative Solutions** \\ \hline How to utilize the fitting ability of deep learning to extract the cover image feature (the master share) in zero watermarking & Applying off-the-shelf CNNs or Transformers and designing extended branches of these architectures \\ \hline How to choose appropriate deep learning models for image watermarking given their different feature extraction abilities and purposes & Adopting pre-trained CNNs or the models in self-supervised learning \\ \hline How to design separate embedding and extraction schemes, given a deep learning feature extractor & To obtain a marked image, fix the pre-trained model, and update the input image with the gradient (produced by a loss ensuring the imperceptibility and extraction integrity) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the challenges and representative solutions in the image watermarking using deep networks for feature transformation. of the other, resulting in enhanced efficiency and effectiveness. The design paradigms and operational frameworks of these methods can be diverse, exhibiting a wide range of structural and functional variations. In these hybrid systems, deep learning typically plays a pivotal role in watermark extraction. The complex and intricate architectures of deep learning models offer enhanced capacity for fitting complex functions, and these models are adept at uncovering intricate patterns and correlations within the watermarked images, thereby facilitating the efficient and accurate extraction of embedded watermarks. The conventional image watermarking calculations, on the other hand, lend stability, reliability, and a degree of interpretability to the process. They serve as a solid foundation upon which the deep learning models can build. [PERSON] et al. [56] employed two convolutional autoencoders to reconstruct a cover image individually. The distinctions between the autoencoder-reconstructed images and the original cover image are integral to their approach. The first autoencoder's reconstruction denotes bit zeros in a binary watermark, while the second represents bit ones. In a different context, [PERSON] et al. [57] tailored a technique specifically for Internet of Things (IoT) applications, utilizing classic spread spectrum for watermark embedding, wherein a key pseudo-noise sequence augments the original signal. The innovation lies in mapping features like spectral flatness, mean, variance, skewness, and kurtosis of the cover image to bit streams, serving as the watermark, enhancing security against eavesdropping attacks by eschewing predefined bit streams. [PERSON] et al. [58] introduced a method where pre-processed grayscale watermark images are integrated into the DCT blocks of cover images. The extraction of these embedded watermarks is facilitated by training CNN, establishing a bridge between conventional and neural approaches. [PERSON] et al. [59] advocated for embedding watermarks into the lifting wavelet domain [60] of cover images. They introduced noise into the marked image and deployed a deep neural network as an extractor, exemplifying the robustness of the infused noise. [PERSON] et al. [61] innovated a technique amalgamating key point detection with deep learning-based image watermarking. Utilizing SURF [62], they delineated scale-invariant embedding regions, placing normalized binary watermarks at their centers in the Y color channels. This aggregated data are routed through an embedding network, yielding the marked Y channel. An extractor network, fine-tuned through training, facilitates watermark retrieval.