Md. Hasan Al Banna, Md Ali Haider, Md. Jaber Al Nahian, M. Islam, K. A. Taher, M. S. Kaiser
{"title":"使用深度CNN和迁移学习方法识别相机模型","authors":"Md. Hasan Al Banna, Md Ali Haider, Md. Jaber Al Nahian, M. Islam, K. A. Taher, M. S. Kaiser","doi":"10.1109/ICREST.2019.8644194","DOIUrl":null,"url":null,"abstract":"The forensic investigation on digital images is to assess the authenticity of images without the embedded security on the images. The camera model identification is the first step for image forensic investigation. The paper proposes the deep Convolutional Neural Network and transfer learning approach for extracting features from an images dataset. An open image dataset of 3900 images have been created using three camera models. Three state-of-the-art machine learning algorithms such as SVM, logistic regression and random forest based classifiers have been used for evaluating identification accuracy.","PeriodicalId":108842,"journal":{"name":"2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Camera Model Identification using Deep CNN and Transfer Learning Approach\",\"authors\":\"Md. Hasan Al Banna, Md Ali Haider, Md. Jaber Al Nahian, M. Islam, K. A. Taher, M. S. Kaiser\",\"doi\":\"10.1109/ICREST.2019.8644194\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The forensic investigation on digital images is to assess the authenticity of images without the embedded security on the images. The camera model identification is the first step for image forensic investigation. The paper proposes the deep Convolutional Neural Network and transfer learning approach for extracting features from an images dataset. An open image dataset of 3900 images have been created using three camera models. Three state-of-the-art machine learning algorithms such as SVM, logistic regression and random forest based classifiers have been used for evaluating identification accuracy.\",\"PeriodicalId\":108842,\"journal\":{\"name\":\"2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICREST.2019.8644194\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICREST.2019.8644194","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Camera Model Identification using Deep CNN and Transfer Learning Approach
The forensic investigation on digital images is to assess the authenticity of images without the embedded security on the images. The camera model identification is the first step for image forensic investigation. The paper proposes the deep Convolutional Neural Network and transfer learning approach for extracting features from an images dataset. An open image dataset of 3900 images have been created using three camera models. Three state-of-the-art machine learning algorithms such as SVM, logistic regression and random forest based classifiers have been used for evaluating identification accuracy.