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.
翻译:深度学习( DL) 展示了其在图像涂料绘制领域的巨大能力,这可以产生可见的可信结果。 同时, 恶意使用高级图像涂料工具( 例如删除关键对象以报告假新闻) 导致图像数据的可靠性受到越来越大的威胁。 为了打击涂料伪造, 我们在此工作中提议建立一个创新的端到端通用图像涂料探测网( GIID-Net), 以像素精度检测被涂漆的区域。 拟议的 GIID- Net 由三个小块组成: 强化块、 提取块和决定块。 具体来说, 强化区的目的是通过使用分级组合的特殊层来提高图像涂色痕迹。 由Neuroral 建筑搜索(NAS) 自动设计的提取块旨在提取实际涂料检测任务的特征。 为了进一步优化提取的暗面显示的暗面特征, 我们将全球和本地的注意模块纳入决策块, 通过测量全球特征的相似性、 提取版图集数据, 本地注意将减少内部差异。 本地研究将最终加强我们GIPA 的深度研究结果 。