Detection of LLM-powered bots using image classification

In the rapidly changing landscape of online social interactions, the presence of automated accounts, or bots, has always posed a significant challenge to maintaining platforms where the information posted is authentic and reliable. The emergence of large language models (LLMs) may exacerbate this problem, as researchers have recently found families of automated accounts that use generative artificial intelligence to produce their posts. This paper focuses on this new type of bot, particularly on detecting them. Using a new detection technique that relies on image classification to distinguish between human and automated accounts, we demonstrate remarkable efficiency in identifying bots whose posts are generated by large language models. Our research improves the results of previous work on the detection of bot accounts powered by generative artificial intelligence.

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Publication Details

Journal
First Monday
Published
2025-05-04
DOI
https://doi.org/10.5210/fm.v30i5.13651
Primary Topic
IoT-based Smart Home Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Detection of LLM-powered bots using image classification

Marinella Petrocchi, Angelo Spognardi, Edoardo Di Paolo
First Monday
IoT-based Smart Home Systems
article

Detection of LLM-powered bots using image classification

Marinella Petrocchi, Angelo Spognardi, Edoardo Di Paolo
article en

Abstract

In the rapidly changing landscape of online social interactions, the presence of automated accounts, or bots, has always posed a significant challenge to maintaining platforms where the information posted is authentic and reliable. The emergence of large language models (LLMs) may exacerbate this problem, as researchers have recently found families of automated accounts that use generative artificial intelligence to produce their posts. This paper focuses on this new type of bot, particularly on detecting them. Using a new detection technique that relies on image classification to distinguish between human and automated accounts, we demonstrate remarkable efficiency in identifying bots whose posts are generated by large language models. Our research improves the results of previous work on the detection of bot accounts powered by generative artificial intelligence.

First Monday
Sapienza Università di Roma
Openalex Percentile: Top 92%
IoT-based Smart Home Systems
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