# Robust Image Inpainting Forensics by Using an Attention-Based Feature Pyramid Network
## 2 Related Works  
### Inpainting Forensics Methods  
Traditional methods for image inpainting forensics usually depend on calculating the similarity between image blocks to identify the location of the inpainted region. [PERSON] et al. [32] proposed a blind detection approach that relies on zero-connectivity and fuzzy membership. Similarly, [PERSON] et al. [33] employed quantization table estimation to evaluate the incongruuity among images for detecting forged images. [PERSON] et al. [34] provided empirical evidence, supporting the notion that traditional methods of image inpainting and forensics are fundamentally similar, which presented a proficient algorithm for detecting forgeries by integrating central pixel mapping, maximum zero-connectivity component labeling, and fragment splicing detection. However, these methods that depend on the similarity of image blocks are limited by some challenging issues. Firstly, the search for highly similar image blocks necessitates the computation of nearly every block in the image, resulting in a significant drain on computational resources. Furthermore, the computational cost escalates rapidly with the increasing image size. Secondly, the high degree of similarity among original image blocks, such as those depicting oceans and deserts, results in a high false alarm rate for traditional methods. Finally, the similarity between image blocks is easily affected by common image post-processing operations, reducing the robustness of traditional methods. Consequently, traditional inpainting forensics methods have poorer performance.  
To address the aforementioned limitations and improve the detection performance, researchers have used deep learning-based image inpainting forensics methods. [PERSON] et al. [20] designed HP-FCN, which incorporated a high-pass pre-filtering module prior to the residual network to mitigate the interference of image content and facilitate the location of inpainting traces. [PERSON] et al. [21] proposed MT-Net, a more versatile tampering location network that extracted tampering traces from the image and subsequently located anomalous regions by assessing the disparities between local features and their reference features. [PERSON] et al. [22] proposed the IID-Net, which utilized the neural architecture search (NAS) algorithm to automatically design feature extraction blocks. [PERSON] et al. [23] improved upon the U-Net architecture by integrating it with feature pyramid networks (FPNs), resulting in a method that effectively detected diffusion-based inpainting traces. [PERSON] et al. [24] built GLFFNet, which incorporated the Swin Transformer and CNN to extract global and local features of inpainted images. [PERSON] et al. [25] built MVSS-Net, which uses multi-view feature learning to jointly exploit tampering boundary artifacts and the noise view of the input. In contrast to the aforementioned deep learning-based methods, our AFPN focuses on the optimization of extracted features to enable the network to effectively acquire and utilize local-to-global contextual information from the inpainted image.