GIID-Net: Generalizable Image Inpainting Detection via Neural Architecture Search and Attention

• URL: http://arxiv.org/abs/2101.07419v2
• Date: Fri, 29 Jan 2021 05:44:31 GMT
• Title: GIID-Net: Generalizable Image Inpainting Detection via Neural Architecture Search and Attention
• Authors: Haiwei Wu and Jiantao Zhou
• Abstract summary: malicious use of advanced image inpainting tools has led to increasing threats to the reliability of image data. To fight against the inpainting forgeries, we propose a novel end-to-end Generalizable Image Inpainting Detection Network (GIID-Net) The proposed GIID-Net consists of three sub-blocks: the enhancement block, the extraction block and the decision block.
• Score: 19.599993572921065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning (DL) has demonstrated its powerful capabilities in the field of image inpainting, which could produce visually plausible results. Meanwhile, the malicious use of advanced image inpainting tools (e.g. removing key objects to report fake news) has led to increasing threats to the reliability of image data. To fight against the inpainting forgeries, in this work, we propose a novel end-to-end Generalizable Image Inpainting Detection Network (GIID-Net), to detect the inpainted regions at pixel accuracy. The proposed GIID-Net consists of three sub-blocks: the enhancement block, the extraction block and the decision block. Specifically, the enhancement block aims to enhance the inpainting traces by using hierarchically combined special layers. The extraction block, automatically designed by Neural Architecture Search (NAS) algorithm, is targeted to extract features for the actual inpainting detection tasks. In order to further optimize the extracted latent features, we integrate global and local attention modules in the decision block, where the global attention reduces the intra-class differences by measuring the similarity of global features, while the local attention strengthens the consistency of local features. Furthermore, we thoroughly study the generalizability of our GIID-Net, and find that different training data could result in vastly different generalization capability. Extensive experimental results are presented to validate the superiority of the proposed GIID-Net, compared with the state-of-the-art competitors. Our results would suggest that common artifacts are shared across diverse image inpainting methods. Finally, we build a public inpainting dataset of 10K image pairs for the future research in this area.

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