| Grant number: | 24/09372-2 |
| Support Opportunities: | Scholarships in Brazil - Scientific Initiation |
| Start date: | August 01, 2024 |
| End date: | July 31, 2025 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
| Principal Investigator: | Sandra Eliza Fontes de Avila |
| Grantee: | Artur Alves Cavalcante de Barros |
| Host Institution: | Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil |
| Associated research grant: | 23/12086-9 - Araceli: Artificial Intelligence in the Fight Against Child Sexual Abuse, AP.R |
Abstract Child sexual abuse materials (CSAM) have reached alarming proportions in the digital age. According to the National Center for Missing and Exploited Children's CyberTipline, over 36 million reports of suspected CSAM were received in 2023, making it a record year. The availability and sharing of such harmful content online not only exacerbate the trauma inflicted upon the victims but also significantly burden law-enforcement agents who have to inspect thousands of files, leading to emotional strain manually. In light of that, there is a need for reliable automated tools that can handle this kind of material securely and efficiently. We aim to design, develop, and deploy a solution based on machine learning to detect CSAM, supporting forensic analysis automatically. This undergraduate research project investigates how scene recognition and representation can be explored for CSAM detection while respecting the limitations of working with CSAM (i.e., low-data regimes and restricted testing). Specifically, we aim to explore how to employ Machine Learning techniques for Scene Graph Generation (SGG) to create helpful scene representations for the CSAM detection task. The primary goals are the following: 1) conduct a comprehensive literature review on the state-of-the-art methods; 2) explore deep Learning models capable of utilizing Scene Graphs as input for scene classification; 3) collaborate with Brazil's Federal Police and Technical-Scientific Police experts to evaluate the models created on real child sexual abuse material. Our research group is at the forefront of such research worldwide, responsible for groundbreaking results associated with digital forensics, machine learning, and computer vision. | |
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