# Review of Image Forensic Techniques Based on Deep Learning
## 4 Robust Image Watermarking Based on Deep Learning
### Robust Image Watermark against Differentiable Attack
#### 4.1.1 Robust Image Watermark against Noise and Filtering Attack
It also utilizes convolutional neural networks (CNNs) for climate factor prediction tasks, which can be trained and evaluated on large-scale datasets to learn different convolutional models using regression models and distance-based loss functions. The results show that the method using deep learning can achieve high precision and extremely high accuracy. It ensures that image data are collected from reliable sources and manually verifies the authenticity of the data, including images collected from AMOS (archive of many outdoor scenes) and Internet webcams with time stamps, camera ids, and location annotations. This ensures that the information collected is true. There are still difficulties in the detection of tampered image metadata: the literature points out that the open-source ExifTool is applied to access and modify image metadata between digital workflows, but this method is easy to be tampered with, so additional evidence is needed to prove the authenticity of image content, such as the analysis of other climate factor images. This indicates that there are still challenges in tampering detection of metadata. To sum up, the advantages of this literature are a reliable data acquisition process, multi-task learning framework, and prediction accuracy based on deep learning. However, it is difficult to detect the tampered image metadata and the data set is not evenly distributed. It can also be a baseline in future works to propose a CNN-based content \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{\begin{tabular}{c} **Watermark Size** \\ **(Container Size)** \\ \end{tabular} } & \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Method (Effect)**} & \multicolumn{2}{c}{**Robustness (Attack, Parameter)**} & \multirow{2}{*}{**Dataset**} \\ \cline{4-4} \cline{6-7}  & & & & **BER (\%)** & & \\ \hline [PERSON] et al. [88] & 30 & GAN-based & GAN (Improving visual quality) & 0.5 (Gaussian blur, \(3\times 3\), \(\sigma_{\rm b}=2.0\)) & \multirow{2}{*}{COCO [99]} \\ [PERSON] et al. [93] & 24 & \begin{tabular}{c} Embedding \\ parameter \\ optimization \\ \end{tabular} & CNN (Feature extraction) & – & 0.9625 & \multirow{2}{*}{\begin{tabular}{c} MPEG-7 CE \\ Shape-1 [100] \\ \end{tabular} } \\ [PERSON] et al. [93] & 24 & \begin{tabular}{c} Embedding \\ parameter \\ optimization \\ \end{tabular} & CNN (Feature extraction) & – & (Gaussian blur, \(3\times 3\), \(\sigma_{\rm b}=1\)) & \multirow{2}{*}{\begin{tabular}{c} MPEG-7 CE \\ Shape-1 [100] \\ \end{tabular} } \\ [PERSON] et al. [94] & 1024 & \begin{tabular}{c} Embedding \\ parameter \\ optimization \\ \end{tabular} & PSO (Selecting best DCT coefficient) & 0 (Gaussian filtering, \(3\times 3\), \(\sigma_{\rm f}=0.5\)) & \multirow{2}{*}{USC-SIPI [101]} \\ [PERSON] et al. [96] & 32 & \begin{tabular}{c} Embedding \\ parameter \\ optimization \\ \end{tabular} & \begin{tabular}{c} SSO (Gaining ideal embedding \\ parameter) \\ \end{tabular} & – & \begin{tabular}{c} (Gaussian noise, \\ \(\sigma_{\rm n}=0.01\)) \\ \end{tabular} & Self-datasets \\ [PERSON] et al. [98] & 32 \(\times\) 32 & \begin{tabular}{c} Embedding \\ parameter \\ optimization \\ \end{tabular} & \begin{tabular}{c} Spatial and channel attention \\ mechanism (Improving robustness) \\ \end{tabular} & 0.09 (Gaussian noise, \(\sigma_{\rm n}=0.05\)) & \
\begin{tabular}{c} 0.9988 \\ (Gaussian noise, \\ \(\sigma_{\rm n}=0.05\)) \\ \end{tabular} &
\begin{tabular}{c} BOSS Base \\ [102], CIFAR \\ 10 [103] \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparison of deep learning-based image watermarking against noise and filtering. retrieval and tampering strategy. [PERSON] et al. [106] proposed an efficient reversible fragile watermarking scheme based on two images (DI-RFWS), which can accurately detect and locate the tamper region in the image. The scheme used a pixel adjustment strategy to embed two secret bits in each host image (HI) pixel to obtain a double-watermarked image (WI).