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Image origin identification for online social networks (OSNs)

机译:在线社交网络的图像原产地识别(OSNS)

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摘要

The rapid development of various online social networks (OSNs) makes it unprecedentedly popular to share photos online. This leads OSNs one of the main provenances of online images. However, the illegal activities on these online contents such as misusing and dissemination widely exist. Under this circumstance, the identification of the origin and the propagation path of an online image is crucial for many forensic applications. In this work, we propose a simple yet effective method to determine the image origin by exploiting the unique traces left by the operations of different OSNs. To this end, we first conduct a comprehensive study on the manipulations that various OSNs perform on uploaded images. Based on the knowledge of these operations, we design a feature vector and eventually train a SVM classifier for identifying where these online images come from. Extensive experimental results are provided, showing that the proposed method achieves very high accuracy of image origin identification, and outperforms the state-of-the-art work.
机译:各种在线社交网络(OSNS)的快速发展使其无法前所未受在线分享照片。这会导致OSNS在线图像的主要种植之一。但是,这些在线内容的非法活动,如滥用和传播广泛存在。在这种情况下,在线图像的起源和传播路径的识别对于许多法医应用至关重要。在这项工作中,我们提出了一种简单而有效的方法,通过利用不同OSN的操作留下的唯一迹线来确定图像来源。为此,我们首先对各种OSNS在上传图像执行的操作进行了全面的研究。基于对这些操作的知识,我们设计了一个特征向量,最终训练SVM分类器,用于识别这些在线图像来自哪里。提供了广泛的实验结果,显示了所提出的方法实现了图像原产地识别的非常高的精度,并且优于最先进的工作。

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