基于稠密连接的深度修复定位网络  

Localization Network of Deep Inpainting Based on Dense Connectivity

作　　者：傅志彬 祁树仁 张玉书 薛明富[1] FU Zhibin;QI Shuren;ZHANG Yushu;XUE Mingfu(College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)

机构地区：[1]南京航空航天大学计算机科学与技术学院,南京211106

出　　处：《信息网络安全》2022年第7期84-93,共10页Netinfo Security

基　　金：国家自然科学基金[62072237];南京航空航天大学研究生科研与实践创新计划项目[xcxjh20211606]。

摘　　要：图像修复是计算机视觉中的一个经典应用。基于深度学习的修复算法可以用较低的成本生成逼真的修复图像。然而,这种强大的算法有潜在的非法或不道德用途,如删除图像中的特定对象以欺骗公众。尽管目前出现许多图像修复的取证方法,但在复杂的修复图像中,这些方法的检测能力仍然有限。基于此,文章提出使用稠密连接的网络有效定位逼真的深度修复图像中的篡改区域。该网络是一种基于稠密连接的编码器和解码器架构,其中引入的稠密连接模块可以更好地捕获在修复图像中细微的篡改痕迹。此外,在稠密连接模块中嵌入Ghost模块、空洞卷积和通道注意力机制可以实现更好的定位性能。实验结果表明,该方法能够在逼真复杂的深度修复图像中有效地识别出篡改区域,并且能够满足对JPEG压缩和旋转的鲁棒性需求。Reconstructing the missing regions of an image is a typical requirement in computer vision.With deep inpainting algorithms,one can generate realistic inpainted images at a very low cost.However,such a powerful tool has potentially illegal or unethical uses,such as removing specific objects from images to deceive the public.Although many forensic methods for image inpainting have been proposed,their detection capabilities are still limited in complex inpainted images.Motivated by that,this paper proposed an efficient network based on dense connectivity to locate tampered regions in a realistic deep inpainting image.The network was an encoder-decoder architecture based on dense connectivity,where the introduced dense connected module can better capture subtle manipulation traces in realistic inpainted images.Furthermore,embedding the Ghost modules,dilated convolutions,and the channel attention mechanism in dense connected blocks could achieve better localization performance.Experiments demonstrate that the proposed method can effectively locate the inpainted regions in sophisticated deep inpainting images,and also show that the method fulfilling the robustness requirements of JPEG compression and rotation.

关 键 词：深度修复 篡改检测 稠密连接 Ghost模块 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]