Image Source Identification Using Convolutional Neural Networks in IoT Environment

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 increasi...

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Main Authors: Yan Wang, Qindong Sun, Dongzhu Rong, Shancang Li, Li Da Xu
Format: Article
Language:English
Published: Hindawi-Wiley 2021-01-01
Series:Wireless Communications and Mobile Computing
Online Access:http://dx.doi.org/10.1155/2021/5804665
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spelling doaj-3c2229ab3aac4b6d990a57ff31d6441e2021-09-20T00:29:52ZengHindawi-WileyWireless Communications and Mobile Computing1530-86772021-01-01202110.1155/2021/5804665Image Source Identification Using Convolutional Neural Networks in IoT EnvironmentYan Wang0Qindong Sun1Dongzhu Rong2Shancang Li3Li Da Xu4Shaanxi Key Laboratory of Network Computing and SecurityShaanxi Key Laboratory of Network Computing and SecurityShaanxi Key Laboratory of Network Computing and SecurityDepartment of Computer ScienceDepartment of IT and DSDigital 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.http://dx.doi.org/10.1155/2021/5804665
collection DOAJ
language English
format Article
sources DOAJ
author Yan Wang
Qindong Sun
Dongzhu Rong
Shancang Li
Li Da Xu
spellingShingle Yan Wang
Qindong Sun
Dongzhu Rong
Shancang Li
Li Da Xu
Image Source Identification Using Convolutional Neural Networks in IoT Environment
Wireless Communications and Mobile Computing
author_facet Yan Wang
Qindong Sun
Dongzhu Rong
Shancang Li
Li Da Xu
author_sort Yan Wang
title Image Source Identification Using Convolutional Neural Networks in IoT Environment
title_short Image Source Identification Using Convolutional Neural Networks in IoT Environment
title_full Image Source Identification Using Convolutional Neural Networks in IoT Environment
title_fullStr Image Source Identification Using Convolutional Neural Networks in IoT Environment
title_full_unstemmed Image Source Identification Using Convolutional Neural Networks in IoT Environment
title_sort image source identification using convolutional neural networks in iot environment
publisher Hindawi-Wiley
series Wireless Communications and Mobile Computing
issn 1530-8677
publishDate 2021-01-01
description 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.
url http://dx.doi.org/10.1155/2021/5804665
work_keys_str_mv AT yanwang imagesourceidentificationusingconvolutionalneuralnetworksiniotenvironment
AT qindongsun imagesourceidentificationusingconvolutionalneuralnetworksiniotenvironment
AT dongzhurong imagesourceidentificationusingconvolutionalneuralnetworksiniotenvironment
AT shancangli imagesourceidentificationusingconvolutionalneuralnetworksiniotenvironment
AT lidaxu imagesourceidentificationusingconvolutionalneuralnetworksiniotenvironment
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