# Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2402.15448 · HTML: https://mlsanity.com/paper/2402.15448/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 120 references that are in the map.
- First posted on arXiv 23 Feb 2024.

## Builds on (most cited first, 22 of 22)

- [Revisiting IM2GPS in the Deep Learning Era](https://mlsanity.com/paper/1705.04838/) (2017)
- [Learning a Dynamic Map of Visual Appearance](https://mlsanity.com/paper/2012.14885/) (2020)
- [ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The Unknown](https://mlsanity.com/paper/2307.10195/) (2023)
- [Image Provenance Analysis at Scale](https://mlsanity.com/paper/1801.06510/) (2018)
- [Hotels-50K: A Global Hotel Recognition Dataset](https://mlsanity.com/paper/1901.11397/) (2019)
- [Revisiting Near/Remote Sensing with Geospatial Attention](https://mlsanity.com/paper/2204.01807/) (2022)
- [Towards seamless multi-view scene analysis from satellite to street-level](https://mlsanity.com/paper/1705.08101/) (2017)
- [Combating Human Trafficking with Deep Multimodal Models](https://mlsanity.com/paper/1705.02735/) (2017)
- [Content-Aware Detection of Temporal Metadata Manipulation](https://mlsanity.com/paper/2103.04736/) (2021)
- [SoK: Exploring the State of the Art and the Future Potential of Artificial Intelligence in Digital Forensic Investigation](https://mlsanity.com/paper/2012.01987/) (2020)
- [The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Faces](https://mlsanity.com/paper/2111.00598/) (2021)
- [Always Lurking: Understanding and Mitigating Bias in Online Human Trafficking Detection](https://mlsanity.com/paper/1712.00846/) (2017)
- [Assessing the Influencing Factors on the Accuracy of Underage Facial Age Estimation](https://mlsanity.com/paper/2012.01179/) (2020)
- [Operations Research and Analytics to Combat Human Trafficking: A Systematic Review of Academic Literature](https://mlsanity.com/paper/2103.16476/) (2021)
- [Hierarchy-Dependent Cross-Platform Multi-View Feature Learning for Venue Category Prediction](https://mlsanity.com/paper/1810.09833/) (2018)
- [Validating the Contextual Information of Outdoor Images for Photo Misuse Detection](https://mlsanity.com/paper/1811.08951/) (2018)
- [Human Age Estimation from Gene Expression Data using Artificial Neural Networks](https://mlsanity.com/paper/2111.02692/) (2021)
- [SoK: Cross-border Criminal Investigations and Digital Evidence](https://mlsanity.com/paper/2205.12911/) (2022)
- [Global forensic geolocation with deep neural networks](https://mlsanity.com/paper/1905.11765/) (2019)
- [Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning](https://mlsanity.com/paper/1907.01427/) (2019)
- [A machine learning pipeline for aiding school identification from child trafficking images](https://mlsanity.com/paper/2106.05215/) (2021)
- [Audio Analytics-based Human Trafficking Detection Framework for Autonomous Vehicles](https://mlsanity.com/paper/2209.04071/) (2022)

## Built on by (most cited first, 3 of 3)

- [Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation](https://mlsanity.com/paper/2412.06781/) (2024)
- [GeoArena: Evaluating Open-World Geographic Reasoning in Large Vision-Language Models](https://mlsanity.com/paper/2509.04334/) (2025)
- [Interpretable Perception and Reasoning for Audiovisual Geolocation](https://mlsanity.com/paper/2603.05708/) (2026)

## Abstract (arXiv)

The task of multimedia geolocation is becoming an increasingly essential component of the digital forensics toolkit to effectively combat human trafficking, child sexual exploitation, and other illegal acts. Typically, metadata-based geolocation information is stripped when multimedia content is shared via instant messaging and social media. The intricacy of geolocating, geotagging, or finding geographical clues in this content is often overly burdensome for investigators. Recent research has shown that contemporary advancements in artificial intelligence, specifically computer vision and deep learning, show significant promise towards expediting the multimedia geolocation task. This systematic literature review thoroughly examines the state-of-the-art leveraging computer vision techniques for multimedia geolocation and assesses their potential to expedite human trafficking investigation. This includes a comprehensive overview of the application of computer vision-based approaches to multimedia geolocation, identifies their applicability in combating human trafficking, and highlights the potential implications of enhanced multimedia geolocation for prosecuting human trafficking. 123 articles inform this systematic literature review. The findings suggest numerous potential paths for future impactful research on the subject.