- Interpol’s Operation Shams II pulled 108,076 faces from jihadist propaganda with help from 11 countries
- AI agents and scripts then deduplicated and quality-checked the set, leaving 6,362 images (~5.9%) for facial recognition
- Interpol’s facial recognition system, launched in 2016, then matched pictures to actual persons, with human verification in tow, and 126 suspected fighters have been identified so far
Interpol says an operation it coordinated in Tunisia in June 2026 used artificial intelligence to work through more than 100,000 faces taken from jihadist propaganda, producing matches against 126 suspected foreign terrorist fighters.
AI did most of the heavy lifting, condensing a mammoth 108,076 images into 6,362 unique, identifiable images that could be run through its decade-old facial recognition system.
This allowed 126 people to be identified on paper as potential terrorists who would still need to be tracked down by the international police organization.
A numbers game, cut down to size
Interpol says programmed AI agents and AI-generated scripts automated parts of the collection and investigative work, removing duplicates and screening out poor-quality faces.
This approach left 6,362 unique, high-quality images, which were then run through the Interpol Facial Recognition System (IFRS). This effectively cut a trove of images into a non-duplicated, actionable list of only ~5.9% of the original set, which could then be used to identify potential terrorists.
This is particularly handy, given that propaganda images often number in the thousands and investigators needed to sift through them manually to identify potential targets, many of which were duplicated by others involved in the same study.
“The success of Operation Shams II demonstrates the value of combining international law enforcement cooperation with responsible AI-assisted analytical capabilities,” noted Interpol’s Director of Counter-Terrorism, María Carmen Muñoz González.
“The operational model provides a strong foundation for supporting future international law enforcement cooperation targeting criminal actors around the world.”
Its automation attempts are not new; In 2019, Digit reported, citing Germany’s Interior Ministry, that Interpol had presented a project called DTECH-Light intended to “detect, extract and analyze digital terrorist content, identify and locate suspects,” with national bureaus asked to upload images of unnamed foreign fighters for comparison.
Interpol does not say what agents it used, which models or vendors sit behind them, or where they ran. “AI-generated scripts,” it says it used, could simply mean automation code written with the help of a large language model rather than any purpose-built system.
For now, an AI is doing the grunt work so that a system launched in 2016, and the officers who check its output, can do theirs considerably faster, which is a significant win in efficiency for an agency that often has to deal with multiple sources of information, even as fears about the tech have been rekindled globally on multiple fronts.
