# Review of Image Forensic Techniques Based on Deep Learning
## 2 Image Forensic Techniques
### Active Forensics
#### 2.2.1 Basic Framework of Robust Image Watermarking Algorithm  
The main basic framework of deep learning-based end-to-end robust watermarking is shown in Figure 3. It consists of three components: embedding layer (including image feature extraction network and watermark feature enhancement network), attack layer, and extraction layer. The model includes two stages of forward propagation and reverse gradient updating when iteratively updating parameters. During forward propagation, the original image and the watermarked image pass through the image feature extraction network and the watermark feature enhancement network, respectively, to extract high-order image features and high-order watermark features. The output results are then fed into the watermark embedding network to obtain the watermarked image. The attack simulation layer includes various types of attacks such as noise and filtering, geometric attacks, and JPEG compression. When the watermarked image passes through the attack simulation layer, different adversarial samples after attacks are generated. The extraction layer performs watermark extraction on the adversarial samples or the watermarked images to obtain the watermark authentication information. Above is the model forward propagation training. In backpropagation, the PSNR loss and SSIM, which measures the similarity of two images from three aspects: grayscale, contrast, and structure loss of the original image and the watermarked image are usually set to improve the similarity between the watermarked image and the original image, which are expressed as Equations (4) and (5), respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Type of Forgery**} & \multicolumn{2}{c}{**Number of Forged**} & \multirow{2}{*}{**Image Format**} & \multirow{2}{*}{**Image Resolution**} \\  & & & & **Images/Authentic Images** & & \\ \hline Columbia color [21] & 2006 & Splicing & 183/180 & BMP, TIF & 757 \(\times\) 568–1152 \(\times\) 768 \\ MICC-F220 [22] & 2011 & Copy-move & 110/1100 & JPG & 480 \(\times\) 722–1070 \(\times\) 800 \\ MICC-F600 [22] & 2011 & Copy-move & 160/440 & JPG, PNG & 722 \(\times\) 480–800 \(\times\) 600 \\ MICC-F200 [20] & 2011 & Copy-move & 700/1300 & JPG & 2048 \(\times\) 1536 \\ CASIA V1 [23] & 2013 & Copy-move, Splicing & 921/800 & JPG & 284 \(\times\) 256 \\ CASIA V2 [23] & 2013 & Copy-move, Splicing & 5123/7200 & JPG, BMP, TIF & 320 \(\times\) 240–800 \(\times\) 600 \\ Carvalho [24] & 2013 & Splicing & 100/100 & PNG & 2048 \(\times\) 1536 \\ CoMoFOD [25] & 2013 & Copy-move & 4800/4800 & PNG, JPG & 512 \(\times\) 512–3000 \(\times\) 2500 \\ COVERAGE [26] & 2016 & Copy-move & 100/100 & TIF & 2048 \(\times\) 1536 \\ Korus [27] & 2017 & Copy-move, Splicing & 220/220 & TIF & 1920 \(\times\) 1080 \\ USCISI [28] & 2018 & Copy-move & 100,000/- & PNG & 320 \(\times\) 240–640 \(\times\) 575 \\ MFC 18 [29] & 2019 & Multiple manipulation & 3265/14,156 & RAW, PNG, BMP, JPG, & 128 \(\times\) 104–7952 \(\times\) 5304 \\ DEFACTO [30] & 2019 & Multiple manipulation & 229,000/- & TIF & 240 \(\times\) 320–640 \(\times\) 6405 \\ IMD 2020 [31] & 2020 & Multiple manipulation & 37,010/37,010 & PNG, JPG & 193 \(\times\) 260–4437 \(\times\) 2958 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Datasets for image forgery detection. At the same time, the MSE loss of extracted watermark and original watermark, which are expressed as Equation (6), is set to improve the accuracy of watermark recovery.