Abstract
The transnational ivory trade continues to drive the decline of elephant populations across Africa, and trafficking networks remain difficult to disrupt. Tusks seized by law enforcement officials carry forensic information on the traffickers responsible for their export, including DNA evidence and handwritten markings made by traffickers. For 20 years, analyses of tusk DNA have identified where elephants were poached and established connections among shipments of ivory. While the links established using genetic evidence are extremely conclusive, genetic data is expensive and sometimes impossible to obtain. But though handwritten markings are easy to photograph, they are rarely documented or analyzed. Here, we present an AI-driven pipeline for extracting and analyzing handwritten markings on seized elephant tusks, offering a novel, scalable, and low-cost source of forensic evidence. Having collected 6,085 photographs from eight large seizures of ivory over a 6-year period (2014-2019), we used an object detection model to extract over 17,000 individual markings, which were then labeled and described using state-of-the-art AI tools. We identified 184 recurring "signature markings" that connect the tusks on which they appear. 20 signature markings were observed in multiple seizures, establishing forensic links between these seizures through traffickers involved in both shipments. This work complements other investigative techniques by filling in gaps where other data sources are unavailable. The study demonstrates the transformative potential of AI in wildlife forensics and highlights practical steps for integrating handwriting analysis into efforts to disrupt organized wildlife crime.
Abstract (translated)
跨国象牙贸易继续导致非洲大象种群的下降，而贩运网络仍然难以破坏。执法官员没收的象牙携带着关于负责其出口的贩运者的法医信息，包括DNA证据和贩运者手工标记的信息。在过去20年里，对象牙DNA的分析已经确定了大象被偷猎的位置，并建立了各批象牙之间的联系。尽管使用遗传证据建立的关联非常有说服力，但遗传数据却昂贵且有时难以获得。然而，虽然手写标记容易拍照，但却很少被记录和分析。在这里，我们提出了一种基于人工智能的手工标记提取和分析被盗大象象牙上手工标记的流程，提供了一个新颖、可扩展且低成本的法医证据来源。在六年期间（2014-2019年）内，从八个大规模象牙没收事件中收集了6,085张照片，我们使用了一种物体检测模型提取出超过17,000个单独的手工标记，并利用最先进的AI工具对其进行标注和描述。我们识别出了184个重复出现的“签名标记”，这些标记将它们出现在其上的象牙连接起来。20个签名标记在多个没收事件中被观察到，通过参与这两个批次运输的相关贩运者建立了各批之间的法医联系。这项研究通过填补其他数据来源不足时出现的信息缺口来补充了其他调查技术。该研究表明人工智能在野生动物法医学中的变革潜力，并强调了将手写分析整合到打击有组织野生动物犯罪努力中的一些实用步骤。
URL
https://arxiv.org/abs/2508.10219
