# Review of Image Forensic Techniques Based on Deep Learning
## 3 Image Forgery Detection Based on Deep Learning
### Image Generic Forgery Detection  
To enable multiple types of tampering detection, the algorithm has been made more generalizable. [PERSON] et al. [63] proposed a two-branch noise and boundary network that used an improved constrained convolution to extract the noise map. It can effectively solve the problem of training instability. The edge prediction module was added to extract the tampered edge information to improve the accuracy of localization. But the detection performance was poor when the tampered image contained less tampered information. [PERSON] et al. [64] designed a multi-view, multi-scale supervised image forgery detection model. The model combined the boundary and noise features of the tampered regions to learn semantic-independent features with stronger generalization, which improved detection accuracy and generalization. But the detection effect was poor when the tampered region was the background region. [PERSON] et al. [65] proposed a network based on signal noise separation to improve the robustness. The signal noise separation module separated the tampered regions from the complex background regions with post-processing noise, reducing the negative impact of post-processing operations on the image and improving the robustness of the algorithm. [PERSON] et al. [66] proposed a network for learning and enhancing multiple tamper traces, fusing multiple features of global noise, local noise, and detailed artifact features for forgery detection, which enabled the algorithm to have high generalization and detection accuracy. However, when the tampering artifacts are reduced, the lack of effective tampering traces results in tampered regions being undetectable. [PERSON] et al. [67] proposed a multimodal transformer framework, which consisted of three main modules: high-frequency feature extraction, an object encoder, and an image decoder, to address the difficulty of capturing invisible subtle tampering in the RGB domain. The frequency features of the image were first extracted, and the tampered regions were identified by combining RGB features and frequency features. The effectiveness of the method was shown on different datasets. To achieve a more refined prediction of tampering masks, a progressive mask-decoding approach is used. [PERSON] et al. [68] proposed a progressive spatio-channel correlation network. The network used two paths, the top-down path acquired local and global features of the image, and the bottom-up path was used to predict the tampered mask. The spatio-channel correlation module was introduced to capture the spatial and channel correlation of features and extract features with global clues to enable the network to cope with various attacks and improve the robustness of the network. To solve the problem of irrelevant semantic information, [PERSON] et al. [69] proposed a progressively-refined neural network. Tampered regions were localized progressively under a coarse-to-fine workflow and rotated residual structure was used to suppress the image content during the generation process. Finally, the refined mask was obtained. To solve the existing problems of low detection accuracy and poor boundary localization, [PERSON] et al. [70] proposed an end-to-end two-stream boundary-aware network for generic image forgery detection and localization. The network introduced an adaptive frequency selection module to adaptively select appropriate frequencies to mine inconsistent statistical information and eliminate the interference of redundant information. Meanwhile, a boundary artifact localization module was used to improve the boundary localization effect. To address the problem of poor generalization ability to invisible manipulation, [PERSON] et al. [71] proposed a channel attention-based image forgery detection framework. The network introduced a channel attention module to detect and localize forged regions using inter-channel interactions to focus more on tampered regions and achieve accurate localization.