# Review of Image Forensic Techniques Based on Deep Learning
## 4 Robust Image Watermarking Based on Deep Learning
### Robust Image Watermark against Non-Differentiable Attack
#### 4.2.2 Robust Image Watermark against Screen-Shooting Attack  
Screen-shooting attacks mainly cause image transmission distortion, brightness distortion, and Moire distortion [128]. To improve the robustness of the screen-shooting attacks, relevant studies have recently been proposed, which can be subdivided into template-based, distortion compensation-based, decoding based on the attention mechanism, keypoint enhancement, transform domain, and distortion simulation. **Template-based:** Template-based watermarking algorithms have a high watermarking capacity. The templates characterizing the watermarking information are embedded in the image in a form similar to additive noise. To ensure robustness, templates usually carry special data distribution features, but conventional template-based watermarking algorithms [117; 129; 130; 131] were designed with low complexity manually and thus could not cope with sophisticated attacks. [PERSON] et al. [132] designed a template-based watermarking algorithm by exploiting the powerful feature learning capability of DNN. In the watermark embedding phase, the embedding template was designed based on the insensitivity of human eyes to the specific chromatic components, the proximity principle, and the tilt effect. In the watermark extraction phase, a two-stage DNN was designed, containing an auxiliary enhancement sub-network for enhancing the embedded watermark features and classification of the sub-network for extracting the internal information of the watermark. **Distortion Compensation-based:** [PERSON] et al. [133] used the method of swapping DCT coefficients to achieve watermark embedding and a distortion compensation extraction algorithm to achieve the robustness of the watermark to photographic processing. Specifically, a line spacing region and a symmetric embedding block were used to reduce the distortion generated by the text. **Decoding Based on Attention Mechanism:** [PERSON] et al. [134] designed a transparency, efficiency, robustness, and adaptability coding to effectively mitigate the conflict between transparency, efficiency, robustness, and adaptability. A color decomposition method was used to improve the visual quality of watermarked images, and a super-resolution scheme was used to ensure the embedding efficiency. Bose Chaudhuri Hocquenghem (BCH) coding [135] and an attention decoding network (ADN) were used to further ensure robustness and adaptivity. **Keypoint Enhancement:** The feature enhanced keypoints are used to locate the watermark embedding region, but the existing keypoint enhancement methods [136; 137] ignore the improvement of the overall algorithm by separating the two steps of keypoint enhancement and watermark embedding. [PERSON] et al. [138] used a convex optimization framework to unify the two steps to improve the accuracy of watermark extraction and blind synchronization of embedding regions effectively. **Transform Domain:** [PERSON] et al. [139] introduced a separable noise layer over the DCT domain in the embedding and extraction layers to simulate screen-shooting attacks. SSIM-based loss functions were introduced to improve imperceptibility. Spatial transformation