Certificate image tampering detection based on contextual semantic information
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%.
国家科技期刊平台
NSTL
万方数据
维普
