Yan Wang, Qindong Sun, Dongzhu Rong, Shancang Li, Lida Xu
{"title":"物联网环境下卷积神经网络图像源识别","authors":"Yan Wang, Qindong Sun, Dongzhu Rong, Shancang Li, Lida Xu","doi":"10.1155/2021/5804665","DOIUrl":null,"url":null,"abstract":"Digital image forensics is a key branch of digital forensics that based on forensic analysis of image authenticity and image content. The advances in new techniques, such as smart devices, Internet of Things (IoT), artificial images, and social networks, make forensic image analysis play an increasing role in a wide range of criminal case investigation. This work focuses on image source identification by analysing both the fingerprints of digital devices and images in IoT environment. A new convolutional neural network (CNN) method is proposed to identify the source devices that token an image in social IoT environment. The experimental results show that the proposed method can effectively identify the source devices with high accuracy.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"26 1","pages":"5804665:1-5804665:12"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Image Source Identification Using Convolutional Neural Networks in IoT Environment\",\"authors\":\"Yan Wang, Qindong Sun, Dongzhu Rong, Shancang Li, Lida Xu\",\"doi\":\"10.1155/2021/5804665\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Digital image forensics is a key branch of digital forensics that based on forensic analysis of image authenticity and image content. The advances in new techniques, such as smart devices, Internet of Things (IoT), artificial images, and social networks, make forensic image analysis play an increasing role in a wide range of criminal case investigation. This work focuses on image source identification by analysing both the fingerprints of digital devices and images in IoT environment. A new convolutional neural network (CNN) method is proposed to identify the source devices that token an image in social IoT environment. The experimental results show that the proposed method can effectively identify the source devices with high accuracy.\",\"PeriodicalId\":23995,\"journal\":{\"name\":\"Wirel. Commun. Mob. Comput.\",\"volume\":\"26 1\",\"pages\":\"5804665:1-5804665:12\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Wirel. Commun. Mob. Comput.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1155/2021/5804665\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wirel. Commun. Mob. Comput.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2021/5804665","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image Source Identification Using Convolutional Neural Networks in IoT Environment
Digital image forensics is a key branch of digital forensics that based on forensic analysis of image authenticity and image content. The advances in new techniques, such as smart devices, Internet of Things (IoT), artificial images, and social networks, make forensic image analysis play an increasing role in a wide range of criminal case investigation. This work focuses on image source identification by analysing both the fingerprints of digital devices and images in IoT environment. A new convolutional neural network (CNN) method is proposed to identify the source devices that token an image in social IoT environment. The experimental results show that the proposed method can effectively identify the source devices with high accuracy.