Bitcoin Burn Addresses: Unveiling the Permanent Losses and Their Underlying Causes Updated 2025-03-28 +Created 2025-03-24
By Mohamed el Khatib and Arnaud Legout.
Both autors were at Inria Centre at Université Côte d'Azur, Mohamed the intern and Arnaud the Inria researcher employee.
Cool, this method could reveal novel P2FKH images:208,656 addresses suspected to be burn addresses because they have a low Shannon entropyUnfortunately their method might not be well suited for finding images, later on:
We propose a methodology to automatically detect burn addresses. We manually classified
Our model identified 7,905 true burn addresses from a pool of 1,283,997,050 addresses with only 1,767 false positive.
Storing images for fun and posterity. We did not observe plain text messages encoded in Bech32 addresses. As our methodology is designed to identify burn addresses with a human-readable structure or easily identifiable patterns, we are not supposed to detect images encoded in burn addresses.
Data for their results can be found at:
They do some really fun hardcore mathy stuff over there!
Ciro Santilli interned at Inria Centre at Université Côte d'Azur in the early 2010's. It was a disaster, largely his own fault, but also due to our broken educational system. But they do have awesome things as well.