# CEASEFIRE: An AI-powered system for combatting illicit firearms trafficking

arXiv: https://arxiv.org/abs/2406.14949 · HTML: https://mlsanity.com/paper/2406.14949/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion](https://mlsanity.com/paper/2003.04490/) (2020)
- [DIET: Lightweight Language Understanding for Dialogue Systems](https://mlsanity.com/paper/2004.09936/) (2020)
- [Fine-grained Visual Classification with High-temperature Refinement and Background Suppression](https://mlsanity.com/paper/2303.06442/) (2023)
- [Self-supervised visual learning in the low-data regime: a comparative evaluation](https://mlsanity.com/paper/2404.17202/) (2024)
- [Self-supervised visual learning for analyzing firearms trafficking activities on the Web](https://mlsanity.com/paper/2310.07975/) (2023)
- [Illicit object detection in X-ray images using Vision Transformers](https://mlsanity.com/paper/2403.19043/) (2024)

## Abstract (arXiv)

Modern technologies have led illicit firearms trafficking to partially merge with cybercrime, while simultaneously permitting its off-line aspects to become more sophisticated. Law enforcement officers face difficult challenges that require hi-tech solutions. This article presents a real-world system, powered by advanced Artificial Intelligence, for facilitating them in their everyday work.