物联网环境下卷积神经网络图像源识别

Yan Wang, Qindong Sun, Dongzhu Rong, Shancang Li, Lida Xu
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引用次数: 3

摘要

数字图像取证是基于对图像真实性和图像内容进行取证分析的数字取证的一个重要分支。智能设备、物联网(IoT)、人工图像和社交网络等新技术的进步,使法医图像分析在广泛的刑事案件调查中发挥越来越大的作用。这项工作的重点是通过分析数字设备的指纹和物联网环境中的图像来识别图像源。提出了一种新的卷积神经网络(CNN)方法来识别社交物联网环境中令牌图像的源设备。实验结果表明,该方法能有效识别源器件,具有较高的识别精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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