The Invisible Crime, The Exploding Data: Leveraging Explainable AI for Automated Victim Identification in Encrypted Trafficking Networks

DOI

10.2991/978-94-6239-610-4_3How to use a DOI?

Keywords

Explainable AI (XAI); Digital Forensics; Human Trafficking; Evidence Triage; OSINT; MLAT; NLP; Encryption; Victim Identification

Abstract

Modern Human Trafficking (HT) exploits the seamless anonymity of digital platforms, migrating the crime scene from the street to the server. This transformation presents a critical forensic paradox: exploitation is ubiquitous, yet evidence is vast, volatile, and encrypted. Traditional manual review methods are overwhelmed, leading to catastrophic delays in victim identification and intervention. This poster unveils these challenges—and the core motivation of analyzing technology-facilitated trafficking footprints (Latonero, 2012)—by unveiling a comprehensive AI-Integrated Cyber-Forensic Pipeline designed specifically to combat this data saturation crisis. The framework goes beyond standard extraction by coupling OSINT Network Mapping with advanced Machine Learning (ML) for rapid evidence triage. We detail the development and validation of an Explainable AI (XAI) model utilizing Natural Language Processing (NLP), which automates the identification of subtle coercion patterns and code-words within billions of communication logs. Concurrently, Computer Vision is applied for rapid, automated authentication and hash-matching of high-risk multimedia content across multiple platforms, effectively addressing evidence duplication and integrity challenges. Our findings demonstrate that this automated triage approach dramatically reduces the time-to-first-relevant-evidence by an estimated 85%, shifting the investigative focus from data collection to intervention. The work critically addresses the non-technical constraints, particularly the friction points of Mutual Legal Assistance Treaties (MLATs) and the ethical imperative for victim-centric forensic protocols that minimize re-traumatization during digital evidence handling. We advocate for the immediate standardization of this validated, high-efficiency framework to effectively scale the global response against digitally facilitated human trafficking.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. Kheekshitha
PY  - 2026
DA  - 2026/05/05
TI  - The Invisible Crime, The Exploding Data: Leveraging Explainable AI for Automated Victim Identification in Encrypted Trafficking Networks
BT  - Proceedings of the First International Conference on Advances in Forensics and Cyber Technologies (ICFACT 2025)
PB  - Atlantis Press
SP  - 14
EP  - 19
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-610-4_3
DO  - 10.2991/978-94-6239-610-4_3
ID  - Kheekshitha2026
ER  -