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Human trafficking remains one of the most heinous crimes, often hidden in plain sight, making it a complex challenge for law enforcement worldwide. The integration of deep learning into advanced surveillance and detection systems presents a promising frontier in the fight against this global issue. This review article explores the transformative impact of deep learning algorithms on surveillance technologies designed to detect patterns and anomalies indicative of human trafficking activities. We delve into various case studies where artificial intelligence (AI)-powered surveillance has not only facilitated the identification and rescue of victims but also significantly hindered the operational capabilities of trafficking networks. By analyzing the deployment of these systems in different contexts, this article assesses their effectiveness, the ethical implications of surveillance, the balance between privacy and security, and the future potential for scaling these technologies. Additionally, we explore the collaborative dynamics between AI technology developers and law enforcement agencies, emphasizing the need for a synergistic approach to maximize the impact of these technologies. This review aims to provide a comprehensive understanding of how cutting-edge deep learning applications are becoming crucial tools in the strategic arsenal against human trafficking, offering a beacon of hope for victims and a significant challenge to traffickers.
Ijiga et al. (Sat,) studied this question.