Shubham Anjankar, Somesh Telang, Khushalsingh Bharadwaj, R. Khandelwal
{"title":"基于维数扩展残差网络的PRNU和噪声融合源相机识别","authors":"Shubham Anjankar, Somesh Telang, Khushalsingh Bharadwaj, R. Khandelwal","doi":"10.47164/ijngc.v13i5.919","DOIUrl":null,"url":null,"abstract":"It might be challenging in the field of image forensics to identify the source camera of a picture. This researchproposes a noise adaptable convolutional neural network-based technique for camera identification. For cameraidentification, the suggested solution combines Photo Response Non-Uniformity (PRNU) noise and Noiseprint.Three parallel dimensionality expanded residual networks with convolutional layers of kernel size 1x1 were puttogether for enhanced feature extraction. The experiment mentioned above uses pictures from the ”Vision Dataset”as its subject matter. The experimental findings demonstrate the effectiveness of the suggested methodology inidentifying the source camera at the brand, model, and device levels. When two of the three networks were fedwith PRNU and one with noiseprint, the best performance was obtained.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"91 1","pages":""},"PeriodicalIF":0.3000,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of Source Camera by Amalgamation of PRNU and Noise Print Using Dimensionality Expansive Residual Network\",\"authors\":\"Shubham Anjankar, Somesh Telang, Khushalsingh Bharadwaj, R. Khandelwal\",\"doi\":\"10.47164/ijngc.v13i5.919\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It might be challenging in the field of image forensics to identify the source camera of a picture. This researchproposes a noise adaptable convolutional neural network-based technique for camera identification. For cameraidentification, the suggested solution combines Photo Response Non-Uniformity (PRNU) noise and Noiseprint.Three parallel dimensionality expanded residual networks with convolutional layers of kernel size 1x1 were puttogether for enhanced feature extraction. The experiment mentioned above uses pictures from the ”Vision Dataset”as its subject matter. The experimental findings demonstrate the effectiveness of the suggested methodology inidentifying the source camera at the brand, model, and device levels. When two of the three networks were fedwith PRNU and one with noiseprint, the best performance was obtained.\",\"PeriodicalId\":42021,\"journal\":{\"name\":\"International Journal of Next-Generation Computing\",\"volume\":\"91 1\",\"pages\":\"\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2022-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Next-Generation Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.47164/ijngc.v13i5.919\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Next-Generation Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47164/ijngc.v13i5.919","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identification of Source Camera by Amalgamation of PRNU and Noise Print Using Dimensionality Expansive Residual Network
It might be challenging in the field of image forensics to identify the source camera of a picture. This researchproposes a noise adaptable convolutional neural network-based technique for camera identification. For cameraidentification, the suggested solution combines Photo Response Non-Uniformity (PRNU) noise and Noiseprint.Three parallel dimensionality expanded residual networks with convolutional layers of kernel size 1x1 were puttogether for enhanced feature extraction. The experiment mentioned above uses pictures from the ”Vision Dataset”as its subject matter. The experimental findings demonstrate the effectiveness of the suggested methodology inidentifying the source camera at the brand, model, and device levels. When two of the three networks were fedwith PRNU and one with noiseprint, the best performance was obtained.