Characteristics and prevalence of fake social media profiles with AI-generated faces
CoRR(2024)
摘要
Recent advancements in generative artificial intelligence (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
1,353 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
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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