# Review of Image Forensic Techniques Based on Deep Learning
## 5 Future Research Directions and Conclusions  
### Future Research Directions  
On the basis of the existing problems in the current research status, this subsection gives future research directions for image forensics. For passive forensics, although tampering detection algorithm techniques have been developed to some extent, there are still some problems of low generalization ability and poor robustness. Because the performance of deep learning-based tampering localization models depends on the training dataset heavily, the performance usually degrades significantly for the test samples from different datasets. It requires us to analyze the intrinsic relationship among images from different sources more deeply and improve the network architecture to learn more effective features. The network model performance also degrades when the images are subjected to certain post-processing attacks, such as scaling, rotation, and JPEG compression. It requires us to perform data augmentation on the data during training to improve the robustness of the model and push the algorithm into practical applications. The current problem of insufficient tampering and low quality of tampering detection datasets seriously affects the development of deep learning-based tampering detection techniques. It is also very important to construct a dataset that meets the actual forensic requirement. Deep learning techniques continue to evolve, bringing many opportunities and challenges to passive image forensics. We should continuously update tampering detection techniques and use more effective network models and learning strategies to improve the accuracy and robustness of algorithms. For active forensics, future research on robust image watermarking will use algorithms based on deep learning. In differentiable attacks, for noise enhancement and filtering attacks, choosing a more stable training framework and training methods is the primary method to effectively solve the current training instability, imperceptibility, and robustness tradeoff, such as using the diffusion model [154; 155; 156] and training codecs in divided stages. To enhance geometry attacks, designing the structure and transformation of restoring synchronization between watermark information and the decoder is an effective way to solve the problem of lack of synchronization between the decoder and watermark destroyed by geometry attacks. For example, an end-to-end deep learning model combined with the scale-invariant feature transform (SIFT) algorithm can effectively improve the robustness of rotation attacks. In the part of a non-differentiable attack, for a JPEG compression attack, the design of a more effective and realistic simulation of differentiable JPEG compression structure is the primary method to solve the problem that non-differentiable JPEG compression can not achieve model training and the poor effect of differentiable simulation JPEG training, for example, the differentiable analog JPEG combined with the attention mechanism to improve the simulation effect. In digital screen camera attacks, designing an effective analog distortion degradation structure is the primary method to solve the problem of poor robustness due to the difficult prediction of screen camera attack distortion. For example, multiple denoising and de-denoizing processes in diffusion model [154; 155; 156]