# Review of Image Forensic Techniques Based on Deep Learning
## 5 Future Research Directions and Conclusions
### Conclusions  
In this review, we synthesize existing research on deep learning-based image forensics from both passive forensics (i.e., tampering detection) and active forensics (i.e., digital watermarking), respectively. For passive forensics, tampered areas of images are detected and located by analyzing the traces left by image tampering. In this survey, we analyze and review the state-of-the-art techniques for deep learning-based image copy-move, splicing, and generic forgery detection. First, we introduce a framework of image forgery detection based on deep learning, evaluation metrics, and commonly used datasets. According to the different types of tampering detection, image forgery detection methods are classified into three categories: image copy-move forgery detection, image splicing forgery detection, and image generic forgery detection. Then, the state-of-the-art algorithms are compared and analyzed in terms of four aspects: type of detection, backbone, robustness performance, and dataset. Finally, future research directions are analyzed in light of the problems of existing tamper detection algorithms. For active forensics, we focus on the robustness of digital watermarking. In the beginning, we introduce a classical end-to-end watermarking model. According to whether the attack type is a differentiable attack or not, we subdivide it into five attacks: noise and filtering attacks, geometric attacks, JPEG attacks, screen-shooting attacks, and agnostic attacks. For noise and filtering attacks, most studies introduce an attack simulation layer in the codec to generate attack counterexamples to improve its robustness, but the joint codec training leads to the degradation of the watermarked image generated by the encoder. For geometric attacks, the construction of geometric invariant features from two perspectives, frequency domain coefficients and zero-watermark, ensure the spatial synchronization of the codec to a certain extent, but it does not combine with the robustness of the other attacks. The robustness of JPEG with low-quality factors still needs to be improved. Distortion simulation is the most commonly used method to enhance screen-shooting attacks in existing studies, but it suffers from the complex composition of actual distortion and low simulation accuracy. For agnostic attacks, reverse ASL effectively improves the accuracy of watermark recovery. Recently, generative AI (for example, chatgpt [157] and DALL-E [158]) technologies are developing rapidly. When it comes to distinguishing between real photos and generated photos, watermarking technology can provide some assistance. However, with the continuous advancement of generative artificial intelligence technology, generated photos are becoming increasingly realistic, making it challenging to rely solely on traditional watermarking technology for accurate differentiation. Generated photos may inherit watermark information from the original photos, making them visually similar to real photos. Additionally, generative AI technology can also generate entirely new photos without any embedded watermarks. Therefore, relying solely on traditional watermarking technology may not be sufficient to differentiate between real photos and generated photos. It may be necessary to combine other image analysis and verification techniques, such as transmembrane state interaction and visual detection algorithms, to improve the accuracy and robustness of identification. **Author Contributions:** [PERSON], [PERSON] and [PERSON] chose the topic and designed the structure of the paper. [PERSON] and [PERSON] sorted out and analyzed passive forensic techniques. [PERSON] and [PERSON] reviewed the robust image watermarking algorithms. [PERSON], [PERSON], [PERSON] and [PERSON] classified the involved algorithms and analyzed the data. [PERSON], [PERSON] and [PERSON] revised the manuscript. [PERSON] performed the project administration and supervision. [PERSON] and [PERSON] is funding acquisition. All authors have read and agreed to the published version of the manuscript.