The extensive spread of fake news on social networks is carried out by a diverse range of users, encompassing private individuals, newspapers, and organizations. With widely accessible image and video editing tools, malicious users can easily create manipulated media. They can then distribute this content through multiple fake profiles, aiming to maximize its social impact. To tackle this problem effectively, it is crucial to possess the ability to analyze shared media to identify the originators of fake news. To this end, multimedia forensics research has advanced tools that examine traces in media, revealing valuable insights into its origins. While combining these tools has proven to be highly efficient in creating profiles of image and video creators, it is important to note that most of these tools are not specifically designed to function effectively in the complex environment of content exchange on social networks. In this paper, we introduce the problem of establishing associations between images and their source profiles as a means to tackle the spread of disinformation on social platforms. To this end, we assembled , an extensive image dataset comprising more than 12,000 images sourced from 21 user profiles across Facebook, Instagram, and Twitter, and we propose three increasingly realistic and challenging experimental scenarios. We present two simple yet effective techniques as benchmarks, one based on statistical analysis of Discrete Cosine Transform (DCT) coefficients and one employing a neural network model based on ResNet, and we compare their performance against the state of the art. Experimental results show that the proposed approaches exhibit superior performance in accurately classifying the originating user profiles.

中文翻译：

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揭示作者身份：将媒体内容链接到社交用户个人资料

虚假新闻在社交网络上的广泛传播是由各种用户进行的，其中包括个人、报纸和组织。借助广泛使用的图像和视频编辑工具，恶意用户可以轻松创建受操纵的媒体。然后，他们可以通过多个虚假个人资料分发此内容，旨在最大限度地提高其社会影响。为了有效解决这个问题，拥有分析共享媒体以识别假新闻来源的能力至关重要。为此，多媒体取证研究拥有先进的工具来检查媒体中的痕迹，揭示其起源的宝贵见解。虽然事实证明，结合这些工具可以非常有效地创建图像和视频创建者的个人资料，但值得注意的是，这些工具中的大多数并不是专门设计用于在社交网络上复杂的内容交换环境中有效运行的。在本文中，我们介绍了在图像与其来源档案之间建立关联的问题，作为解决社交平台上虚假信息传播的一种手段。为此，我们组装了一个广泛的图像数据集，其中包含来自 Facebook、Instagram 和 Twitter 21 个用户个人资料的 12,000 多张图像，并提出了三个日益现实且具有挑战性的实验场景。我们提出了两种简单但有效的技术作为基准，一种基于离散余弦变换 (DCT) 系数的统计分析，另一种采用基于 ResNet 的神经网络模型，并将它们的性能与最先进的技术进行比较。实验结果表明，所提出的方法在准确分类原始用户配置文件方面表现出优越的性能。