Image Inpainting Detection Based on High-Pass Filter Attention Network

Can Xiao, Feng Li, Dengyong Zhang, Pu Huang, Xiangling Ding, Victor S. Sheng

Research output: Contribution to journalArticlepeer-review


Image inpainting based on deep learning has been greatly improved. The original purpose of image inpainting was to repair some broken photos, such as inpainting artifacts. However, it may also be used for malicious operations, such as destroying evidence. Therefore, detection and localization of image inpainting operations are essential. Recent research shows that high-pass filtering full convolutional network (HPFCN) is applied to image inpainting detection and achieves good results. However, those methods did not consider the spatial location and channel information of the feature map. To solve these shortcomings, we introduce the squeezed excitation blocks (SE) and propose a high-pass filter attention full convolutional network (HPACN). In feature extraction, we apply concurrent spatial and channel attention (scSE) to enhance feature extraction and obtain more information. Channel attention (cSE) is introduced in upsampling to enhance detection and localization. The experimental results show that the proposed method can achieve improvement on ImageNet.

Original languageEnglish
Pages (from-to)1146-1154
Number of pages9
JournalComputer Systems Science and Engineering
Issue number3
StatePublished - 2022


  • Image inpainting detection
  • channel attention
  • full convolutional network
  • high-pass filter
  • spatial attention


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