基于上下文语义信息的凭证篡改检测研究
Certificate image tampering detection based on contextual semantic information
李佩 1王伟 1刘勇 1王义1
作者信息
- 1. 蚂蚁集团 重庆蚂蚁消费金融有限公司,重庆 400060
- 折叠
摘要
在消费金融服务场景下,存在用户逾期还款的情况.在逾期协商还款过程中,少量用户篡改图像凭证,实现不当得益.这些篡改集中在个人信息、印章、出具单位等具有很强的上下文语义联系内容上.基于此,在传统空域直接像素空间 RGB和频域离散余弦变换(discrete cosin trans-form,DCT)作为判别特征的基础上,引入了文字块、印章块的位置关系和反卷积网络,实现了一种包含语义关系的端到端全卷积神经网络模型.该模型在天池 2022 年"真实场景篡改图像检测挑战赛"的数据集上,相对于传统模型平均交并比有 3.97%的提升,在实际凭证图像篡改判断中,提升了 3.7%的篡改检测准确率.
Abstract
During consumer financial services,there is a headache with overdue repayment.When negotiating with customers,a small number of them try to use tampered certificate images to achieve illegal benefits.These tampering focuses on content with strong contextual semantic connections such as personal information,seals,and issuing units.Based on the traditional spatial domain RGB and frequency domain DCT as discriminative features,the position of text blocks,seal blocks,and deconvolution network to realize an end-to-end fully convolutional neural network that includes semantic relations are introduced.Compared with the traditional models,it has a 3.97%higher mIoU in"Tianchi's 2022 Real Scene Tampering Image Detection Challenge"dataset.In our service scenario,the accuracy of tampering detection has been improved by 3.7%.
关键词
图像篡改检测/篡改区域定位/语义分割/神经网络/深度学习Key words
image tampering detection/image tampering localization/semantic segmentation/neural network/deep learning引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据