Uncovering the authorship: Linking media content to social user profiles

• Published in: Pattern recognition letters 2024-05, Vol.181, p.9-15
• Main Authors: Baracchi, Daniele, Shullani, Dasara, Iuliani, Massimo, Giani, Damiano, Piva, Alessandro
• Format: Article
• Language: English
• Subjects: Dataset; Disinformation detection; Fake news; Multimedia forensics; Social media; User profiling
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2024.03.008

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 SocialNews, 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. •We introduce SocialNews, a novel dataset for disinformation detection online.•Images were sourced primarily from social profiles of news agencies around the world.•The goal is to automatically identify the profile of the user that shared an image.•We introduce two benchmarks methods: a DCT-based one and a ResNet-based one.•Experiments show that the proposed benchmark methods outperform the state of the art.