# A Survey of Generative AI for Detecting Pedophilia Crimes
## 4 Literature Review: AI Approaches for Detection
### Machine Learning Models
The study identified five key grooming features using a stepwise regression combined with three methods and paired t-tests. The model demonstrated impressive accuracy, reaching 95%, underscoring its potential to detect online grooming conversations. However, the study faced challenges, including the lack of child-specific language within grooming exchanges and the overlap of grooming features within non-grooming dialogues. [PERSON] et al. [21] introduced a two-stage methodology that uses a SVM and a feature ensemble strategy to identify predators in online chat interactions. The framework operates in two phases: the first focuses on detecting predatory conversations, while the second differentiates between predators and their victims. The study integrates lexical and behavioral features, with the BoW representation emerging as the most compelling feature set. The SVM was chosen because of its proven effectiveness in addressing text classification challenges. The PAN12 Sexual Predator Identification dataset, initially developed for a competition at the 2012 Conference and Labs of the Evaluation Forum (CLEF), served as the basis for the experiments. The results show that the BoW approach, combined with tf-idf term weighting, provides superior performance for the PCI and VPD tasks. Specifically, the method achieved F0.5 scores of 0.9893 for PCI and 0.9798 for VPD, underscoring its high accuracy and reliability. Although the feature ensemble technique shows significant potential, the study highlights the importance of mitigating the impact of noisy features to optimize performance. [PERSON] et al. [22] introduced a semi-supervised anomaly detection framework to identify predatory chat documents. This framework effectively addresses the limitations of supervised techniques that rely on extensive labeled data for predatory and non-predatory categories. Their approach uses a one-class SVM trained exclusively on predatory chat data, thereby eliminating the need to label non-predatory conversations. The researchers evaluated their methodology using the PAN-2012 dataset, a well-known dataset curated for sexual predator identification tasks. The data was preprocessed using RapidMiner, while the SVM model was implemented using LibSVM. The experimental results showed that the one-class SVM outperformed the performance of the baseline Naive Bayes classifier and achieved results comparable to those of the binary SVM classifier, reaching an F-measure of 75% after incorporating a noise removal step. This study underscores the practicality of semi-supervised anomaly detection for identifying predatory chat documents, highlighting its ability to perform effectively with significantly less labeled data. [PERSON] et al. [23] proposed a method for classifying and identifying sexually exploitative content within textual exchanges on these platforms. The primary goal of this study was to develop an algorithm that can accurately detect grooming content while also assessing the performance of the classification technique. The detection models employed include n-gram and sequence models to enhance the classification precision. The data was gathered from several widely used social media platforms, including YouTube, Instagram, and Twitter. The results show that the n-gram model achieved an accuracy rate of 81.6%, while the sequence model had a slightly lower accuracy of 69.8%. The study acknowledges the challenges of detecting grooming behavior due to the sheer volume of online content shared daily, which complicates the identification process. It demonstrates how raw social media datasets can be used to develop and apply classification algorithms. [PERSON] et al. [24] proposed a privacy-preserving, cross-device federated learning framework designed for the early detection of sexual predators. The core objective of the study was to develop a decentralized, privacy-preserving method using FL to detect sexual predators in online communications at an early stage.