= Bitcoin Burn Addresses: Unveiling the Permanent Losses and Their Underlying Causes
{title2=2025-03-18}
https://arxiv.org/pdf/2503.14057
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:
> We propose a methodology to automatically detect burn addresses. We manually classified
208,656 addresses suspected to be burn addresses because they have a low Shannon entropy
> Our model identified 7,905 true burn addresses from a pool of 1,283,997,050 addresses with only 1,767 false positive.
Unfortunately their method might not be well suited for finding images, later on:
> 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:
* https://github.com/cirosantilli/bitcoin-inscription-indexer#low-entropy-addresses-khatib-legout-csv
* https://github.com/cirosantilli/bitcoin-inscription-indexer/blob/master/data-manual/low-entropy-addresses-khatib-legout.csv
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