# A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
## 3 Comprehensive Survey of Deep Learning-Based Image Watermarking
### Methods Using Deep Networks for Feature Transformation  
As illustrated in Figure 3, watermarking procedures within this category predominantly utilize deep neural networks for feature transformations. Both cover and marked images undergo transformations facilitated by these networks, leading to the creation of distinct feature spaces. Subsequent watermark embedding and extraction are executed within these defined spaces. A common expectation is the robustness of the transformed domains, implying that even minor alterations to the marked images should yield consistent or nearly identical feature values. Numerous methods have adopted the concept of deep networks for feature transformation in the context of deep learning-based zero watermarking. [PERSON] et al. [26] employed CNNs to extract features from cover images, which were then integrated with a permuted binary watermark sequence via an exclusive or XOR operation to create a master share. The same CNN processes a test image to extract features, which are subsequently XORed with the master share to extract the watermark. An appropriate key can ensure the identification of the watermark. [PERSON] et al. [51] extended this foundational approach by adding fully connected layers to draw shallow features from various convolutional layers, enhancing the master share creation process. Their introduction of a shrinkage module facilitates the automatic learning of soft thresholding for each feature channel, enhancing feature extraction precision. To optimize the feature space, they focused on eliminating redundancy by learning inter and intra-feature weights and incorporated a noise layer  
Figure 3: General process of the deep networks for feature transformation. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline
**Challenges** & **Representative Solutions** \\ \hline The noise layer needs to be differentiable & Performing a two-stage training scheme \\ \hline The non-differentiable nature and low-performance issues of JPEG & Including differentiable JPEG simulations in the noise layer \\ \hline Special challenging noises such as the camera resampling & Including simulated camera distortions in the noise layer \\ \hline Models have more robustness to trained noises than those not included in the noise layer & Developing strategies that do not require noise lists during the training, e.g., an invariance layer that sieves out extraneous information, or an adversarial network to automatically attack the extractor \\ \hline Aiming at enhanced and improved overall model performance & Introducing innovative architectures and training paradigms aligns with the nuanced processes of embedding, extraction, and feature transformation, mirroring the strategic design inherent in conventional watermarking \\ \hline Including multimedia for cover images while maintaining high performance & Designing special neural networks to process the multi-modal features and robustness \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of the challenges and representative solutions in the embedder-extractor image watermarking. during feature training to increase robustness. [PERSON] et al. [52] enhanced this methodology by introducing a chaotic encryption algorithm to encrypt the watermark before the XOR operation, enhancing security. They also adopted the Swin Transformer [53] to generate features for master share creation, achieving a feature space that is invariant to geometric distortions and enhances the robustness of the watermarking process. Another research direction in this category involves employing pre-trained deep neural networks, wherein the training of input data is performed to yield the intended marked images. In this scenario, the pre-trained weights remain static, and the alterations in the input are driven toward achieving specific objectives. The resultant marked image is visually analogous to the original cover image, yet reveals the embedded watermark upon undergoing feature extraction by the deep network. [PERSON] et al.