SPAN: Spatial Pyramid Attention Network for Image Manipulation Localization

被引:169
作者
Hu, Xuefeng [1 ]
Zhang, Zhihan [1 ]
Jiang, Zhenye [1 ]
Chaudhuri, Syomantak [2 ]
Yang, Zhenheng [3 ]
Nevatia, Ram [1 ]
机构
[1] Univ Southern Calif, Los Angeles, CA 90089 USA
[2] Indian Inst Technol, Bombay, Maharashtra, India
[3] Facebook AI, Menlo Pk, CA USA
来源
COMPUTER VISION - ECCV 2020, PT XXI | 2020年 / 12366卷
关键词
D O I
10.1007/978-3-030-58589-1_19
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We present a novel framework, Spatial Pyramid Attention Network (SPAN) for detection and localization of multiple types of image manipulations. The proposed architecture efficiently and effectively models the relationship between image patches at multiple scales by constructing a pyramid of local self-attention blocks. The design includes a novel position projection to encode the spatial positions of the patches. SPAN is trained on a generic, synthetic dataset but can also be fine tuned for specific datasets; The proposed method shows significant gains in performance on standard datasets over previous state-of-the-art methods.
引用
收藏
页码:312 / 328
页数:17
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