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A hybrid convolutional architecture for accurate image manipulation localization at the pixel-level | |
Zhang, Yixuan1,2; Zhang, Jiguang3![]() ![]() | |
发表期刊 | MULTIMEDIA TOOLS AND APPLICATIONS
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ISSN | 1380-7501 |
2021-01-22 | |
期号 | 80页码:23377–23392 |
摘要 | Advanced image processing techniques can easily edit images without leaving any visible traces, making manipulation detection and localization for forensics analysis a challenging task. Few studies can simultaneously locate tampered objects accurately and refine contours of tampered regions effectively. In this study, we propose an effective and novel hybrid architecture, named Pixel-level Image Tampering Localization Architecture (PITLArc), which integrates the advantages of top-down detection-based methods and bottom-up segmentation-based methods. Moreover, we provide a typical fusion implementation of our proposed hybrid architecture on one outstanding detection-based method (two-stream faster region-based convolutional neural network (RGB-N)) and two segmentation-based methods (Multi-Scale Convolution Neural Networks (MSCNNs) and Dual-domain Convolutional Neural Networks (DCNNs)) to evaluate the effectiveness of the proposed architecture. The three methods can be integrated into our proposed PITLArc to significantly improve their performance. Other detection and segmentation algorithms (not limited to the three aforementioned methods) can also be integrated into our architecture to improve their performance. Moreover, a Dense Conditional Random Fields (DenseCRFs)-based post-processing method is introduced to further optimize the details of tampered regions. Experiments validate the effectiveness of the proposed architecture. |
关键词 | Manipulation localization Top-down detection Bottom-up segmentation DenseCRFs |
DOI | 10.1007/s11042-020-10211-1 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | NSFC[U1636102] ; NSFC[U1736214] ; NSFC[61802393] ; NSFC[61872356] ; National Key Technology RD Program[2016QY15Z2500] ; Project of Beijing Municipal Science & Technology Commission[Z181100002718001] |
项目资助者 | NSFC ; National Key Technology RD Program ; Project of Beijing Municipal Science & Technology Commission |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000610019400012 |
出版者 | SPRINGER |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/42898 |
专题 | 多模态人工智能系统全国重点实验室_三维可视计算 |
通讯作者 | Xu, Shibiao |
作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Zhang, Yixuan,Zhang, Jiguang,Xu, Shibiao. A hybrid convolutional architecture for accurate image manipulation localization at the pixel-level[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2021(80):23377–23392. |
APA | Zhang, Yixuan,Zhang, Jiguang,&Xu, Shibiao.(2021).A hybrid convolutional architecture for accurate image manipulation localization at the pixel-level.MULTIMEDIA TOOLS AND APPLICATIONS(80),23377–23392. |
MLA | Zhang, Yixuan,et al."A hybrid convolutional architecture for accurate image manipulation localization at the pixel-level".MULTIMEDIA TOOLS AND APPLICATIONS .80(2021):23377–23392. |
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