Seahorses and other marine species smuggled in suitcases could be detected with AI

“The wildlife trade is cruel and unethical,” says Dr Vanessa Pirotta of Macquarie University, lead author of the new paper published in Frontiers in Ocean Sustainability. “For many, this may be the first time they have heard of illegal marine wildlife trafficking. Wildlife trafficking is not limited to the species we are most familiar with, such as rhino horn or elephant ivory. We take advantage of this World Oceans Day to make this problem visible.”

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It is estimated that the illegal trade in marine fauna moves billions of dollars each year and represents a serious threat to endangered animals. The transport of animals for human consumption, medicinal, ornamental or as pets endangers the survival of populations that live in a precarious balance, while animals that are trafficked alive could escape and become invasive species in other ecosystems.

However, detecting ongoing illegal trafficking is easier said than done, making it difficult not only to stop it, but also to quantify its environmental impact.

The team repurposed existing X-ray CT scanners, which are used at many airports to detect explosives or biosecurity threats. These scanners take multiple X-rays of a single object, creating a 3D image of its contents.

By using a neural network to train an algorithm capable of recognizing commonly trafficked species in these images, the scientists hoped to create a system that could automatically mark suitcases for inspection.

The scientists chose to work with shark fins, seahorses and sea cucumbers. Shark fins are in high demand as food, while dried seahorses are marketed for traditional medicine.

Smuggling of sea cucumbers is less frequently recorded, although we know that they are often illegally overfished; Researchers believe that sea cucumber smuggling is more common than we can currently prove.

They performed a total of 298 scans of 20 sea cucumber, 30 seahorse and 18 shark fin samples, many of them from wildlife trafficking seizures. Five different scans were created for each sample in different positions and contexts, as well as scans containing several different samples.

The scientists also scanned samples under conditions that mimic smugglers’ tactics (wrapping them in cans or clothing, or hiding them in children’s toys) and added some of their scans to CT images of suitcases that had been scanned without contraband merchandise, a technique called Threat Imaging. This helps simulate real-life circumstances, where samples may be found hidden in luggage.

The scientists used these images to train the algorithm to recognize shark fins, sea cucumbers, and seahorses, and then tested the algorithm with a subset of images that had never been provided to it before.

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The algorithm had an overall effectiveness of 92%: 95% in detecting shark fins, 96% in detecting seahorses, and 86% in detecting sea cucumbers. The false positive rate was 13%: 2% for shark fins, 1% for sea cucumbers, and 9% for seahorses.

This high accuracy suggests that this automatic detection algorithm could be a powerful tool to intercept shipments that currently evade existing controls, helping to cut off trade routes and secure convictions for those who traffic marine wildlife.

However, an effective automatic detection program for these species is only part of the solution. Many other species are also illegally trafficked, and false positives will continue to require manual checks. Additionally, not all airports have access to 3D CT scanners, which are expensive; others still rely on 2D scanners. Automatic detection will complement existing detection methods, rather than replace them.

“We can only simulate real traffic scenarios based on what has been previously detected,” concludes Pirotta. “AI is not the ultimate solution for detection, nor a substitute for human detection and sniffer dogs.”