# 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
**Embedding Parameter Optimization:** The position and strength of the embedding parameters determine the algorithm performance directly. [PERSON] et al. [93] performed an iterative simulation of the attack on the watermarking system. But it can only obtain one bit of watermark information from a sub-block. [PERSON] et al. [94] first subjected the host image to DCT transformation to extract the human eye-insensitive LH sub-band and HL sub-band. Particle swarm optimization (PSO) was used to find the best DCT coefficients and the best embedding strength to improve the imperceptibility and robustness of the watermarking algorithm. However, due to the training overfitting of PSO [95], its model generalized and achieved good robust performance only on the experimental dataset. [PERSON] and [PERSON] [96] combined fuzzy, backpropagation neural networks and shark optimization algorithms. However, refs. [94; 96] had the problem of training overfitting. To improve the training overfitting problem, [PERSON] et al. [97] introduced a two-dimensional image information entropy loss to enhance the ability of the model to generate different watermarks, ensuring that the model was always able to assign enough information to a single host image for different watermark inputs and the extractor can extract the watermark information completely, therefore enhancing the dynamic randomness of the watermark embedding. [PERSON] et al. [98] specifically adopted an end-to-end robust image watermarking algorithm framework, known as the embed-attack-extract paradigm. In the embedding layer of the network, it incorporated the channel spatial attention mechanism. As a result, during training, after forward and backward propagation for parameter updates, the embedding layer's parameters were able to focus on more effective channel and spatial information, which also made the model focus on the optimization of increasing the accuracy of the extracted watermark. To sum up, this optimization of the watermark embedding parameters contributed to enhancing the model's resistance to noise and filtering attacks. Deep learning-based image watermarking against noise and filtering attack algorithms and performance comparison are described in Table 3. Table 3 describes the methods from five aspects: watermark size (container size), category, method (effect), robustness, and dataset, where \(\sigma_{\text{b}}\), \(\sigma_{\text{f}}\), and \(\sigma_{\text{n}}\) represents the variance of Gaussian blur, Gaussian filtering, and Gaussian noise, respectively. **Fragile Watermark with Content Tampering Detection:** The above is based on robust watermarking to achieve the active forensics of noise and filtering attacks, the following is based on fragile watermarking to achieve the copyright protection of noise and filtering attacks and content tampering proof. [PERSON] [104] proposed a fragile watermarking scheme based on logical mapping to effectively detect and locate the tampered region of watermarked images. It took advantage of the sensitivity of logical mapping to generate watermark bits by performing a logical XOR operation between the first intermediate significant bit (ISB) and the watermark bit, and embedding the result in the rightmost least significant bits (LSBs). The watermarked image obtained by this method is of high quality and good imperceptibility with an average peak signal-to-noise ratio (PSNR) of 51.14 dB. It can effectively detect and locate the tampering area from the image, and can resist both intentional and unintentional attacks to a certain extent. However, it cannot recover the tampered region. [PERSON] et al. [105] studies content tampering in multimedia watermarking. It introduces a feature association model based on a multi-task learning framework that can relatively detect multiple modifications based on location and time. Multi-task learning frameworks leverage the interrelationships between multiple related problems or tasks and can effectively learn from shared representations to provide better models.