Leveraging self-supervised learning for scene classification in child sexual abuse imagery

Author(s)

Valois, Pedro H. V. ; Macedo, Joao ; Ribeiro, Leo S. F. ; dos Santos, Jefersson A. ; Avila, Sandra

• Document type: Journal article
• Source: FORENSIC SCIENCE INTERNATIONAL-DIGITAL INVESTIGATION; v. 53, p. 11-pg., 2025-06-01.

Abstract

Crime in the 21st century is split into a virtual and real world. However, the former has become a global menace to people's well-being and security in the latter. The challenges it presents must be faced with unified global cooperation, and we must rely more than ever on automated yet trustworthy tools to combat the ever-growing nature of online offenses. Over 10 million child sexual abuse reports are submitted to the US National Center for Missing & Exploited Children every year, and over 80% originate from online sources. Therefore, investigation centers cannot manually process and correctly investigate all imagery. In light of that, reliable automated tools that can securely and efficiently deal with this data are paramount. In this sense, the scene classification task looks for contextual cues in the environment, being able to group and classify child sexual abuse data without requiring to be trained on sensitive material. The scarcity and limitations of working with child sexual abuse images lead to self-supervised learning, a machine-learning methodology that leverages unlabeled data to produce powerful representations that can be more easily transferred to downstream tasks. This work shows that self-supervised deep learning models pre-trained on scene-centric data can reach 71.6% balanced accuracy on our indoor scene classification task and, on average, 2.2 percentage points better performance than a fully supervised version. We cooperate with Brazilian Federal Police experts to evaluate our indoor classification model on actual child abuse material. The results demonstrate a notable discrepancy between the features observed in widely used scene datasets and those depicted on sensitive materials. (AU)

• FAPESP's process: 23/12086-9 - Araceli: Artificial Intelligence in the Fight Against Child Sexual Abuse
• Grantee: Sandra Eliza Fontes de Avila
• Support Opportunities: Regular Research Grants
• FAPESP's process: 22/14690-8 - Few-Shot Scene Recognition, a New Proxy Task for Child Sexual Abuse Material Detection
• Grantee: Leo Sampaio Ferraz Ribeiro
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 20/09838-0 - BI0S - Brazilian Institute of Data Science
• Grantee: João Marcos Travassos Romano
• Support Opportunities: Research Grants - Research Centers in Engineering Program
• FAPESP's process: 13/08293-7 - CCES - Center for Computational Engineering and Sciences
• Grantee: Munir Salomao Skaf
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC