A painting stolen from a church in Italy in 2004 was discovered by chance after more than two decades, thanks to artificial intelligence, which was able to identify the work despite changes in its origin.
The discovery was made in October 2024 during an Antiquities Fair in Parma, Italy, where police officers were photographing the pieces displayed and comparing them with a database containing over a million items reported as stolen.
Just hours later, the artificial intelligence system issued a signal for a painting featuring a young woman carrying a parchment. At first glance, the work did not seem suspicious, but technology had discovered similarities to a painting reportedly stolen two decades ago.
Investigations confirmed that it was a work of the 20th century VII, representing St. Agnes and was stolen on the night of August 11, 2004 by the Virgin Pottens church near Naples.
To conceal the identity of the picture, the authors had intervened in it, replacing the lamb containing the sanctuary with a parchment while removing the aureola and the palm branch.
However, the artificial intelligence system, called Sowads, was able to identify the work by analyzing its colors, shapes, and structure.
Technology was introduced in November 2023 and has since helped Italian police recover over 300 large paintings and sculptures, as well as more than 165 thousand other cultural objects. Their total value is estimated at over 203m euros.
The system is already used to analyze auction catalogs and online platforms, constantly looking for works that can match items reportedly stolen.
However, artificial intelligence does not replace investigators, as identification of similarities must be followed by proper verifications and investigations.
After more than 20 years, St. Agnes’ painting returned to the church on May 20, 2025, where it is expected to be restored and restored to its home country.
A work of art that had been changed to conceal identity was discovered by technology that is playing an increasingly important role in protecting cultural heritage.
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Source Layer: Signal