Unsustainable trade in wildlife is a major threat to biodiversity and is now increasingly prevalent in digital marketplaces and social media. With the sheer volume of digital content, the need for automated methods to detect wildlife trade listings is growing. These methods are especially needed for the automatic identification of wildlife products, such as ivory. We developed machine learning-based object recognition models that can identify wildlife products within images and highlight them. The data consists of images of elephant, pangolin, and tiger products that were identified as being sold illegally or that were confiscated by authorities. Specifically, the wildlife products included elephant ivory and skins, pangolin scales, and claws (raw and crafted), and tiger skins and bones. We investigated various combinations of training strategies and two loss functions to identify the best model to use in the automatic detection of these wildlife products. Models were trained for each species while also developing a single model to identify products from all three species. The best model showed an overall accuracy of 84.2% with accuracies of 71.1%, 90.2% and 93.5% in detecting products derived from elephants, pangolins, and tigers, respectively. We further demonstrate that the machine learning model can be made easily available to stakeholders, such as government authorities and law enforcement agencies, by developing a smartphone-based application that had an overall accuracy of 91.3%. The application can be used in real time to click images and help identify potentially prohibited products of target species. Thus, the proposed method is not only applicable for monitoring trade on the web but can also be used e.g. in physical markets for monitoring wildlife trade.

Wildlife trafficking remains a critical global issue, significantly impacting biodiversity, ecological stability, and public health. Despite efforts to combat this illicit trade, the rise of e-commerce platforms has made it easier to sell wildlife products, putting new pressure on wild populations of endangered and threatened species. The use of these platforms also opens a new opportunity: as criminals sell wildlife products online, they leave digital traces of their activity that can provide insights into trafficking activities as well as how they can be disrupted. The challenge lies in finding these traces. Online marketplaces publish ads for a plethora of products, and identifying ads for wildlife-related products is like finding a needle in a haystack. Learning classifiers can automate ad identification, but creating them requires costly, time-consuming data labeling that hinders support for diverse ads and research questions. This paper addresses a critical challenge in the data science pipeline for wildlife trafficking analytics: generating quality labeled data for classifiers that select relevant data. While large language models (LLMs) can directly label advertisements, doing so at scale is prohibitively expensive. We propose a cost-effective strategy that leverages LLMs to generate pseudo labels for a small sample of the data and uses these labels to create specialized classification models. Our novel method automatically gathers diverse and representative samples to be labeled while minimizing the labeling costs. Our experimental evaluation shows that our classifiers achieve up to 95% F1 score, outperforming LLMs at a lower cost. We present real use cases that demonstrate the effectiveness of our approach in enabling analyses of different aspects of wildlife trafficking.

Scientists applied machine vision models and were able to deduce from the context of an image if it pertained to the sale of a live animal. These methods make it possible to flag the posts which may be selling animals illegally. Illegal wildlife trade is estimated to be a multi-billion dollar industry where hundreds of species are traded globally. A considerable proportion of the illegal wildlife trade now uses online marketplaces to advertise and sell live animals or animal products as it can reach more buyers than previously possible. With the trade happening across the internet it is extremely challenging to manually search through thousands of posts and methods for automated filtering are needed.

Pangolins, also known as scaly anteaters, are currently the most trafficked mammal species. Criminals can be resourceful and unrelenting in their efforts to find a way around obstacles. Wildlife traffickers are no exception. Today's trade in wildlife and wildlife products has shifted from physical markets to online marketplaces where traffickers apply e-commerce business models and use encrypted messages in an attempt to evade detection by law enforcement. While the move towards online platforms started several years before the Covid-19 pandemic, the restrictions imposed to contain the virus accelerated this digital transformation.

A special effects and technology company in San Francisco is pitching a surprisingly lifelike animatronic dolphin to aquariums and marine parks in China to help them deal with the country's recent ban on wildlife trade. The dolphin was developed by Edge Innovations, a company founded by special effects veteran Walt Conti, who previously worked on The Abyss, Anaconda, Deep Blue Sea, The Perfect Storm, among many others. The current prototype was modeled after an adolescent bottlenose dolphin, weighs around 595 pounds, and can swim for 10 continuous hours on a single battery charge. Edge Innovations has developed a shockingly lifelike animatronic robot that it's pitching to marine parks and aquariums in China as a cheaper alternative to real dolphins The dolphin was designed to mimic the skeletal structure of a real dolphin, and uses internal bladders and weight deposits to further match a real dolphin's swimming movements - and its teeth have been given a light yellow staining for extra realism. The dolphin requires a human operator to swim and can't operate autonomously, according to a report in Gizmodo..

In a new article published in the journal Conservation Biology, scientists from the University of Helsinki, Digital Geography Lab, argue that methods from artificial intelligence can be used to help monitor the illegal wildlife trade on social media. Dr. Enrico Di Minin, a conservation scientist at the University of Helsinki, who leads an interdisciplinary research group where methods from artificial intelligence are being developed and used to investigate the supply chain of the illegal wildlife trade in an innovative and novel way, stresses the importance of such novel methods to identify relevant data on the illegal wildlife trade from social media platforms. "Currently, the lack of tools for efficient monitoring of high-volume social media data limits the capability of law enforcement agencies to curb illegal wildlife trade," says Dr. Di Minin "Processing such data manually is inefficient and time consuming, but methods from artificial intelligence, such as machine-learning algorithms, can be used to automatically identify relevant information. Despite their potential, approaches from artificial intelligence are still rarely used in addressing the biodiversity crisis," he says. Many social media platforms provide an application programming interface that allows researchers to access user-generated text, images and videos, as well as the accompanying metadata, such as where and when the content was uploaded, and connections between the users.

Remember that endangered anteater-esque ball of scales from Favreau's recent film, The Jungle Book? Over one million pangolins have likely been poached and illegally traded in the last decade, especially because of their importance in Chinese medicine and food. And the internet hasn't exactly helped its plight. As wildlife trade monitoring network TRAFFIC reports, their researchers investigated 39 Chinese e-commerce websites over June and July 2016; a single survey in the first month alone detected 153 ads for pangolin scales and meat and live specimen from 94 traders across six sites. Virtual wildlife trafficking poses a serious threat to not just the pangolin.