{"title":"基于深度分类器和小波变换的假图像检测","authors":"Stanislaw Osowski, Maciej Golgowski","doi":"10.26636/jtit.2023.4.1336","DOIUrl":null,"url":null,"abstract":"The paper presents the computer system for detecting deep fake images in video films. The system is based on applyingcontinuous wavelet transformation combined with the ensemble of classifiers composed of a few convolutional neural networks of diversified architecture. Three different forms of forged images taken from the Face-Forensics++ database are considered in numerical experiments. The results of experiments on the application of the proposed system have shown good performance in comparison to other actual approaches to this problem.","PeriodicalId":38425,"journal":{"name":"Journal of Telecommunications and Information Technology","volume":"72 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep Classifiers and Wavelet Transformation for Fake Image Detection\",\"authors\":\"Stanislaw Osowski, Maciej Golgowski\",\"doi\":\"10.26636/jtit.2023.4.1336\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper presents the computer system for detecting deep fake images in video films. The system is based on applyingcontinuous wavelet transformation combined with the ensemble of classifiers composed of a few convolutional neural networks of diversified architecture. Three different forms of forged images taken from the Face-Forensics++ database are considered in numerical experiments. The results of experiments on the application of the proposed system have shown good performance in comparison to other actual approaches to this problem.\",\"PeriodicalId\":38425,\"journal\":{\"name\":\"Journal of Telecommunications and Information Technology\",\"volume\":\"72 \",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Telecommunications and Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.26636/jtit.2023.4.1336\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Telecommunications and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26636/jtit.2023.4.1336","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
Deep Classifiers and Wavelet Transformation for Fake Image Detection
The paper presents the computer system for detecting deep fake images in video films. The system is based on applyingcontinuous wavelet transformation combined with the ensemble of classifiers composed of a few convolutional neural networks of diversified architecture. Three different forms of forged images taken from the Face-Forensics++ database are considered in numerical experiments. The results of experiments on the application of the proposed system have shown good performance in comparison to other actual approaches to this problem.