# A Survey of Generative AI for Detecting Pedophilia Crimes
## 1 Introduction
Despite its potential, the use of generative AI in this highly sensitive area is fraught with challenges. Ethical issues related to privacy, algorithmic bias, the risk of erroneous identification, and the threat of misuse by malicious parties must be carefully navigated to ensure the technology is applied responsibly. While the application of AI to combat online child exploitation is an active area of research, existing literature has predominantly focused on specific aspects of the problem. Previous studies have successfully applied traditional Machine Learning (ML) techniques, such as Support Vector Machines (SVMs) and rule-based systems, to detect predatory language in chat logs, often using established datasets like PAN2012 and Perverted Justice. More recent comprehensive surveys, such as the work by [PERSON] et al. [7], have provided extensive overviews of these ML-based grooming detection techniques. Evidence assessments by researchers like [PERSON] et al. have systematically reviewed the broader role of AI in identifying CSAM and aiding law enforcement. This body of work establishes the relevance and potential of AI in this critical domain. However, a significant portion of this research predates the recent explosion of advanced Generative AI and Large Language Models (LLMs). While existing surveys have cataloged past methodologies, there is a clear gap in providing a forward-looking synthesis focused specifically on the unique capabilities and challenges presented by modern Generative AI. This paper aims to fill that gap by offering a distinct contribution that differs from the state-of-the-art in several key ways. Our primary synthesis and novel perspective are consolidated in the later sections of this paper. Specifically, this survey distinguishes itself by:  
* Identifying and analyzing the specific limitations of each individual work surveyed, presenting these findings systematically in our comparative analysis table. * Providing a dedicated discussion of the collective technological and ethical risks associated with deploying Generative AI in such a sensitive context, including privacy concerns, algorithmic bias, and the potential for misuse. * Proposing concrete and structured directions for future research, which are directly derived from the identified gaps and limitations in the current body of work. To fully appreciate the advancements and capabilities of Generative AI in this domain, it is important to first understand the foundational approaches from which it evolved. Therefore, this review begins by examining the traditional research methods and classical Machine Learning models that have historically been applied to this problem. This foundational context provides a critical baseline for evaluating the paradigm shift introduced by Generative AI, particularly Large Language Models, which are the primary focus of the later sections of this analysis. This paper explores the promise of generative AI in the fight against pedophilic crimes. We aim to provide an in-depth review of current research, methodologies, and challenges while exploring potential avenues for future development and ethical implementation. This study aims to address the following Research Question (RQ):  
* RQ: How can generative AI be effectively used to detect and prevent pedophilial-related crimes in digital environments? This work will critically analyze the existing literature and examine different methodologies and approaches. It will highlight both the challenges and future opportunities of using generative AI to protect children from online sexual exploitation. Used responsibly, AI has the potential to significantly enhance child protection in the digital age. The remainder of this paper is organized as follows. Section 2 provides fundamental insights into the phenomenon of online grooming and the complexities of generative AI paradigms. Section 3 outlines the methodological framework used for a comprehensive review of the relevant literature. Section 4 presents the synthesized findings from the literature review.