# A Survey of Generative AI for Detecting Pedophilia Crimes
## 1 Introduction  
Pedophilia is a severe psychosocial disorder characterized by a persistent and often exclusive sexual attraction to children who have not yet reached puberty. Pedophilia causes long-lasting trauma, leading to conditions such as anxiety, depression, PTSD, and difficulty in forming stable relationships [1]. Addressing this problem requires advanced tools, such as AI, to detect and prevent abusive behavior early on. Online grooming is defined as the process by which an adult manipulates a child through the internet, often with malicious intent, particularly for sexual exploitation. Groomers deploy various tactics such as offering attention, affection, kindness, and gifts to lure and seduce their victims. Core grooming components--including psychological assessment, enticement, cyber exploitation, control, and self-preservation--are critical for identifying grooming behavior. These stages have been extensively studied by [PERSON] et al. (2022) [2], who demonstrated that grooming behavior can be detected through automated systems. However, the nuanced nature of human interactions continues to pose significant challenges. Unfortunately, the rapid expansion of digital platforms and social media has created new avenues for perpetrators to target and exploit minors. These environments, which provide anonymity and uninterrupted communication, have become breeding grounds for grooming, the dissemination of CSAM, and the orchestration of sexual offenses againstchildren. The multifaceted role of artificial intelligence in combating child sexual abuse, from identifying CSAM to aiding law enforcement investigations, has been systematically reviewed in recent evidence assessments, underscoring both its potential and the challenges involved in this complex domain [3]. This evolving digital landscape presents formidable challenges to both law enforcement and child protection organizations, who strive to detect and prevent these heinous crimes. As discussed by [PERSON] and [PERSON] (2022), the global reach and accessibility of online platforms have dramatically transformed the scale and complexity of child sexual exploitation, pushing the boundaries of conventional detection and prevention methods [4]. Traditional detection methods, such as victim reports and device-based investigations, are reactive and inadequate for the scale and complexity of online exploitation. The vast volume of data and evolving tactics of perpetrators require innovative and proactive solutions. Generative AI, a rapidly advancing field of artificial intelligence, offers a promising solution in the fight against online child abuse. Artificial intelligence is increasingly recognized as a pivotal technology in this context, with applications ranging from the automated detection of predatory behavior using natural language processing to the detection of CSAM through image recognition systems [5]. Generative AI's ability to process large amounts of data, identify complex behavioral patterns, and generate new outputs makes it a valuable tool for detecting suspicious behavior and uncovering indicators of child sexual exploitation. This technology holds significant promise in several key areas [6]:  
Automated CSAM Detection--AI algorithms can analyze images, videos, and text to flag both known and previously unseen instances of child sexual abuse material, including within encrypted environments. Online Predator Identification--By analyzing communication trends, linguistic markers, and digital behavior patterns, AI systems can help identify individuals engaged in grooming or exploitation attempts. Proactive Risk Assessment--Although still highly speculative and surrounded by profound ethical concerns, AI models could theoretically be used to identify behavioral markers associated with an increased risk of offending. However, any such application would require extensive ethical safeguards and governance to ensure that preventive actions are both responsible and justified.