{"title":"使用WLBP描述符的源相机识别","authors":"Nasme Zandi, F. Razzazi","doi":"10.1109/MVIP49855.2020.9187484","DOIUrl":null,"url":null,"abstract":"In this paper we introduce a camera identification method using WLBP texture descriptor. This descriptor has previously been used for texture and face classifiers. In the proposed method, we proposed to use WLBP operator in camera classification application to identify the imaging camera. In our method, the two-dimensional histogram of Weber’s features and LBP for camera identification are investigated. For this purpose, experiments were conducted on Dresden database. The proposed method has reached the accuracy of 99.52% on nine digital cameras of different models. In compressed JPEG images with the compression quality factor of 70% the method reached the accuracy of 89.04%. The results indicate that the proposed method has a high degree of accuracy in comparison to other proposed method and exhibits relatively good robustness to compression.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Source Camera Identification Using WLBP Descriptor\",\"authors\":\"Nasme Zandi, F. Razzazi\",\"doi\":\"10.1109/MVIP49855.2020.9187484\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we introduce a camera identification method using WLBP texture descriptor. This descriptor has previously been used for texture and face classifiers. In the proposed method, we proposed to use WLBP operator in camera classification application to identify the imaging camera. In our method, the two-dimensional histogram of Weber’s features and LBP for camera identification are investigated. For this purpose, experiments were conducted on Dresden database. The proposed method has reached the accuracy of 99.52% on nine digital cameras of different models. In compressed JPEG images with the compression quality factor of 70% the method reached the accuracy of 89.04%. The results indicate that the proposed method has a high degree of accuracy in comparison to other proposed method and exhibits relatively good robustness to compression.\",\"PeriodicalId\":255375,\"journal\":{\"name\":\"2020 International Conference on Machine Vision and Image Processing (MVIP)\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-02-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Machine Vision and Image Processing (MVIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MVIP49855.2020.9187484\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Machine Vision and Image Processing (MVIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MVIP49855.2020.9187484","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Source Camera Identification Using WLBP Descriptor
In this paper we introduce a camera identification method using WLBP texture descriptor. This descriptor has previously been used for texture and face classifiers. In the proposed method, we proposed to use WLBP operator in camera classification application to identify the imaging camera. In our method, the two-dimensional histogram of Weber’s features and LBP for camera identification are investigated. For this purpose, experiments were conducted on Dresden database. The proposed method has reached the accuracy of 99.52% on nine digital cameras of different models. In compressed JPEG images with the compression quality factor of 70% the method reached the accuracy of 89.04%. The results indicate that the proposed method has a high degree of accuracy in comparison to other proposed method and exhibits relatively good robustness to compression.