# A Survey of Generative AI for Detecting Pedophilia Crimes
## 4 Literature Review: AI Approaches for Detection
### Deep Learning and Large Language Models
Their study aimed to overcome the limitations of traditional methods, such as reliance on manual feature extraction and classifier design, and to address the lack of a universal model that can consistently perform across different datasets and languages. The study employed three distinct datasets: the PAN 2012 dataset for detecting sexual predatory chats and the Roman Urdu and Urdu datasets for identifying abusive language. The fine-tuned Llama 2 model delivered exceptional performance on all three datasets, surpassing the results of existing state-of-the-art methods. The study highlighted the potential of this approach for detecting online sexual predators and offensive language in both English and non-English languages. [PERSON] et al. [31] introduced a framework to combat online grooming on social networking services by integrating OCR with a deep learning-based NLP model, KcELECTRA, specifically tailored for the Korean language. The objective was to accurately detect grooming conversations, which are characterized by a six-stage process of manipulation, to protect vulnerable users. The methodology involves extracting text from SNS chat images using OCR and then classifying the content using the fine-tuned KcELECTRA model. The model was trained on extensive Korean datasets from AI-Hub, including SNS conversations and hate speech. The proposed KcELECTRA model was benchmarked against several others, including RoBERTa, BLOOMz, and DeBERTa-v3. Experimental results showed that the framework achieved a high accuracy of 0.953, outperforming existing transformer-based models. Despite this success, the study faced challenges such as the limited availability of Korean-specific grooming data and the insufficient accuracy of OCR technology, which can misinterpret nuanced expressions. Key conclusions highlight the framework's competitive efficiency and its potential to create safer online environments by focusing on linguistic cueswhile upholding user privacy. Future work will focus on enhancing multimodal detection, addressing data privacy through synthetic data generation, and improving context-aware classification by analyzing longer conversational sequences. [PERSON] et al. [32] aimed to develop and optimize algorithms for the early detection of online grooming in Korean conversations, using the PAN12 Korean dataset. Researchers evaluated seven BERT-based models and three LLMs, comparing a conventional \"window method\" against a novel \"memory method\". The window method references a fixed number of consecutive previous sentences, while the memory method retrieves sentences based on semantic similarity. This approach was designed to enhance accuracy and speed by focusing on relevant context. To address memory constraints during training, the Quantized Low-Rank Adaptation (QLoRA) method was applied to the LLMs,  
The results demonstrated that the optimal method depends on the model's architecture. For instance, with a window size of 10, the Roberta-large model achieved an F\({}_{1}\) score of 0.85, accuracy of 0.81, and speed of 0.68. In contrast, the memory method with the same parameters resulted in an F\({}_{1}\) score of 0.84, accuracy of 0.79, and speed of 0.71. The study also introduced two key evaluation metrics: the latency-weighted F\({}_{1}\) (F\({}_{\text{latency}}\)), which combines F\({}_{1}\) score and detection speed, and the Human-to-Model Ratio (HMR), which compares model detection speed to human judgment. A key conclusion was that the memory method is more suitable for BERT-based models in long conversations, while the window method is highly effective for LLMs due to their larger token capacity. [PERSON] et al. [33] advanced the crucial field of online grooming detection by systematically comparing the performance of traditional machine learning with modern LLMs, specifically focusing on how predator tone influences detection accuracy.