# A Survey of Generative AI for Detecting Pedophilia Crimes
## 4 Literature Review: AI Approaches for Detection
### Identifying Grooming Behaviors and Risk Factors  
This subsection examines research that focuses on the non-technical aspects of online grooming. The studies discussed here explore the identification of behavioral indicators exhibited by perpetrators and the assessment of risk factors associated with potential victims, including the development of specific measurement tools. Although it is not AI-based, this foundational work is intentionally included as it provides the essential groundwork for AI-driven detection systems. It effectively defines the specific'red flag' behaviors and risk factors that advanced models, including Generative AI, aim to identify and automate the detection of. This serves as a critical baseline for the subsequent analysis.  
[PERSON] et al. [9] conducted a study to identify early indicators of sexual grooming by comparing the reported behaviors between adults who had experienced CSA and those who had not. The study included 913 participants, of whom 411 reported CSA and 502 did not. Those who reported CSA completed the SGS-V based on their personal experiences. In contrast, those who did not report CSA were randomly assigned to one of three categories: family member, non-family member, or community member. They were then asked to complete a modified version of the SGS-V about the adult male with whom they had the most frequent contact before the age of 18.  
The study revealed a range of sexual grooming behaviors that distinguished the actions of adults involved in CSA from those who were not. The nature of the relationship between the adult and the child played a critical role in this distinction. One of the significant findings was the challenge of recognizing sexual grooming behaviors before the abuse takes place, as these actions often mirror everyday adult-child interactions. An important conclusion drawn from the study was that red flag grooming behaviors, particularly those aimed at desensitizing the child to physical touch and sexual content, can be identified in cases of CSA and are essential for prevention efforts.  
[PERSON] et al. [14] conducted research to develop and validate the Online Grooming Risk (OGR) scale, a tool designed to detect online grooming in its earliest stages. The study focused on identifying and assessing risk factors associated with online grooming, such as low self-esteem and inadequate family support. The researchers also explored therelationship between these risk factors and the likelihood of minors engaging in online sexual interactions with strangers.  
The validation process involved a sample of 316 adolescents, with data analysis carried out using a non-parametric method known as Structural Equation Modeling based on Partial Least Squares (PLS-SEM). Key findings from the study revealed that the OGR scale is both a reliable and valid measure of online grooming risk. Furthermore, the research highlighted that family support is a protective factor, while low self-esteem emerged as a significant risk factor. The study emphasized the importance of identifying early warning signs of harmful behavior to prevent online grooming.