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Characteristics and prevalence of fake social media profiles with AI-generated faces (2401.02627v2)

Published 5 Jan 2024 in cs.CY, cs.AI, and cs.SI

Abstract: Recent advancements in generative AI have raised concerns about their potential to create convincing fake social media accounts, but empirical evidence is lacking. In this paper, we present a systematic analysis of Twitter (X) accounts using human faces generated by Generative Adversarial Networks (GANs) for their profile pictures. We present a dataset of 1,420 such accounts and show that they are used to spread scams, spam, and amplify coordinated messages, among other inauthentic activities. Leveraging a feature of GAN-generated faces -- consistent eye placement -- and supplementing it with human annotation, we devise an effective method for identifying GAN-generated profiles in the wild. Applying this method to a random sample of active Twitter users, we estimate a lower bound for the prevalence of profiles using GAN-generated faces between 0.021% and 0.044% -- around 10K daily active accounts. These findings underscore the emerging threats posed by multimodal generative AI. We release the source code of our detection method and the data we collect to facilitate further investigation. Additionally, we provide practical heuristics to assist social media users in recognizing such accounts.

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Citations (6)
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Summary

  • The paper introduces a novel GAN-based detection method using consistent eye positioning to identify fake profiles.
  • It analyzes a dataset of 1,353 Twitter accounts to estimate that 0.021%-0.044% of active users employ AI-generated profile images.
  • The study highlights the need for improved detection systems and regulatory measures to mitigate digital disinformation.

Analysis of Fake Social Media Profiles with AI-Generated Faces

In the paper "Characteristics and prevalence of fake social media profiles with AI-generated faces," the authors provide a comprehensive examination of the use of AI-generated images in the creation of fake social media profiles. The paper particularly focuses on Twitter accounts utilizing Generative Adversarial Networks (GANs) to fabricate realistic human faces for profile pictures. The paper introduces a dataset of 1,353 Twitter accounts identified as fake, which engage in activities such as spreading scams, spam, and propagating coordinated disinformation campaigns.

The authors emphasize the growing sophistication of generative AI tools, particularly GANs, in producing highly realistic images that are increasingly indistinguishable from genuine human photographs. The paper leverages a unique characteristic of GAN-generated faces — precise and consistent positioning of eyes — to develop a method for detecting such profiles. This technique, coupled with manual human annotation, forms the basis of their detection methodology.

Using this approach, the researchers estimate the prevalence of profiles with GAN-generated faces among active Twitter users to be between 0.021% and 0.044%, translating to approximately 10,000 daily active accounts. This figure is compelling, highlighting a tangible presence of AI-generated profiles despite their ostensibly low proportion. The paper provides a nuanced understanding of the implications of generative AI misuse, suggesting that these artificial profiles are utilized not just for deception but as instruments for coordinated activities that challenge the authenticity of social media interactions.

The paper's findings illuminate the broader implications of AI in the digital information ecosystem. As AI-generated content proliferates, the potential for its misuse in creating sophisticated inauthentic accounts becomes a significant concern for social media platforms and users alike. This underscores the necessity for advanced detection systems to safeguard digital spaces.

The authors explicitly present the challenges in detecting AI-generated images with existing technological tools, advocating for improved algorithms that consider the nuanced aspects of facial landmark placements characteristic of GAN-generated content. Additionally, the paper briefly touches upon the regulatory and educational strategies necessary to mitigate the risks posed by the proliferation of such deceptive profiles. Engaging social media platforms in policy-making and user education appears crucial for developing resilience against AI-enhanced disinformation.

In summary, this paper offers substantial insights into the tactics and prevalence of GAN-generated profiles on social media platforms, with implications for future research and technological development. The authors provide open access to their source code and datasets, inviting further exploration and refinement of detection methodologies across varying contexts and platforms. Looking forward, as generative technologies continue to evolve, the paper accentuates the importance of cross-disciplinary collaboration in addressing the potential threats to digital information integrity.

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