一種古籍文字圖像篡改檢測(cè)識(shí)別模型

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中圖分類號(hào)：TP751文獻(xiàn)標(biāo)志碼：A

A Model for Detection and Recognition of Tampered Ancient Text Images

LI Yongbo 1 ， QIAN Yonggang 2 ， LIU Qin 1 ， MA Yuqi 1 ， WU Sheng 1 ， YU Xianping 1 ， CHEN Shanxiong ?1，3 （1.Collge of Computer and Information Science，Southwest University，Chongqing 40O715，China；2. Information Center， ChongqigVocational CollgeofIntellgntEngineeing，Chongqing 40216O，China；3.KeyLaboratoryofEthnic Language Intellgent Analysisand SecurityGovernance，MinistryofEducation，Minzu UniversityofChina，Beijing1Ooo81，China）

Abstract：Toeffectively detectandrecognize tampered textinancientdocument images，atampering detectionand recognition model named MDAS-Net，which canbe used for the character images of ancient texts，was proposed.A fuse atention block was introduced inthe edge-supervised branch to enhance multi-scale feature extraction of imagecontent. Additionally，to improve feature integration between theedge-supervised branch and the noise-sensitive branch，acrossbranch feature transfer modulenamedE-2-N/N-2-EHelp Block wasdesigned，whichfacilitatedeffectiveinformation exchangeand yields higher-qualityfused features.To verifytheefectivenessofthemodel，adatasetofancient textimage tampering was created，and comparative experimentsandablation experimentswereconducted in combination with the Text in Tampered Images （TTI）dataset.The experimental results show that MDAS-Net achieves promising performance in tampered region detection，with an area under curve of receiver operating characteristic（AUC）of O.852 and an F1 （204 score of O.784，confirming its practical value in ancient text image tampering detection.

Keywords： image processing；feature fusion；detection of tampered image；ancient text image；deep learning

在文字圖像篡改檢測(cè)和識(shí)別任務(wù)中，模型須要通過像素級(jí)別的精確定位來區(qū)分篡改圖像和真實(shí)圖像，這意味著模型不僅要識(shí)別被篡改的區(qū)域，而且要精確地定位這些區(qū)域。（剩余14253字）