# A Survey of Generative AI for Detecting Pedophilia Crimes
## 5 Benefits of Generative AI in Detecting Pedophilia
### Automating CSAM Detection  
The advanced architectures that underpin Generative AI, such as Transformers, are revolutionizing the automated detection of CSAM. For textual content, LLMs can directly analyze discussions for harmful patterns. While detection of visual and audio content is fundamentally a discriminative task, state-of-the-art classification models increasingly leverage the same robust architectures that originated in the generative domain. This technological capability outperforms traditional methods, offering superior speed and comprehensive coverage by facilitating the rapid identification of illicit material. For example, the work of [PERSON] et al. [6] presents a BERT-based LLM that was trained using real abuse reports from the 'Te Protejo' hotline in Colombia. This model successfully classifies complaint types, severity, and urgency. This example illustrates how generative \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Intervention Timeline** & **Description** & **Approach/Key Feature** & **Studies** \\ \hline \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & Research focused on identifying grooming behaviors and risk factors without creating an automated detection system. This work is foundational for later & Development and validation of scales (SGS-V, OGR) through surveys and statistical analysis. & [PERSON] et al. [9], [PERSON] et al. [14] \\ \hline \multirow{4}{*}{Retrospective Analysis} & Analysis of static, historical datasets of conversations or content. Useful for investigations, pattern discovery, and benchmarking models. & Classification and analysis of established datasets like PAN2012, Perverted Justice, or social media data. & [PERSON] et al. [17], [PERSON] et al. [21], [PERSON] et al. [22], [PERSON] et al. [26], [PERSON] et al. [29], [PERSON] et al. [33] \\ \hline \multirow{4}{*}{Real-Time/Early Detection} & Monitoring and analyzing conversations as they occur to flag suspicious activity immediately, enabling intervention before harm escalates. & Use of sliding window techniques, federated learning on user devices, or memory-based context retrieval. & [PERSON] et al. [24], [PERSON] et al. [25], [PERSON] et al. [27], [PERSON] et al. [32] \\ \hline \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & Systems designed specifically to handle different languages or content types beyond text & Fine-tuning language-specific models (e.g., Korean) or using OCR to extract text from images. & [PERSON] et al. [30], [PERSON] et al. [31] \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & Studies focused not on building a detector, but on evaluating the inherent capabilities and safety of existing, general-purpose LLMs for this task. & Prompt-based testing of commercial LLMs like ChatGPT, PaLM2, etc. & [PERSON] and [PERSON] [11] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: **Classification of Intervention Timelines. ** This table classifies the surveyed studies based on their primary intervention timeline, distinguishing between foundational risk factor identification, retrospective analysis of historical data, and real-time or early detection systems. models can be fine-tuned to assist with real-world investigations, thereby reducing the workload and emotional burden on analysts. Innovative approaches are exploring the use of metadata, such as file paths, to train machine learning models for CSAM detection, which could offer advantages in scenarios with strict legal and ethical limitations on accessing direct CSAM imagery [34]. Such methods aim to maintain high accuracy while minimizing the need to handle sensitive visual content directly during model development. The proliferation of AI-generated CSAM (AIG-CSAM) itself presents new vectors of harm. It complicates detection efforts, necessitating a review of both technological and policy-based solutions to combat this evolving threat [35].