Child Sexual Abuse Material (CSAM) Detection

This project proposes the exploration of visual cues for the detection of Child Sexual Abuse Material (CSAM). The acquisition and distribution of child sexual abuse imagery are some of the most important concerns for legislative systems and law enforcement agencies around the world. There is a great demand for automatic detection approaches due to the fast growing distribution of novel content over the internet such that human experts can no longer handle the manual inspection. Our goal in the long run is to expedite the work of law enforcement agents directly working with CSAM.

Title: Child Sexual Abuse Material (CSAM) Detection
Duration: 2020-current.

Related Publications:

  • Macedo, João, Filipe Costa, and Jefersson A. dos Santos. “A benchmark methodology for child pornography detection.” 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2018. [Publication] [PDF]
  • Laranjeira da Silva, Camila, et al. “Seeing without Looking: Analysis Pipeline for Child Sexual Abuse Datasets.” 2022 ACM Conference on Fairness, Accountability, and Transparency. 2022. [Publication] [PDF]

Datasets:

Thesis and Dissertations:

  • Macedo, João. “Pedophilia Detection based on Age Estimation From Faces” (2018). [PDF (soon)]
  • Valois, Pedro Henrique Vaz. “Leveraging Self-Supervised Learning for Scene Recognition in Child Sexual Abuse Imagery” (2022). [PDF (soon)]