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The gold standard for redaction is to replace the redacted text with nonsense of similar length. Otherwise, you retain precise metrics for the redacted text. Wi
by raphlinus 8y ago
The gold standard for redaction is to replace the redacted text with nonsense of similar length. Otherwise, you retain precise metrics for the redacted text. With guesses or context you can fill those in with high probability. In an extreme case, the redaction might be a name of let's say a member of Congress, so the candidates can be narrowed down to a tiny number. There's considerable work on this, notably the "Declassification Engine" [0]. I believe it would be possible to apply even better word models (such as GPT-2) to improve the results even more.
I'm interested in whether this document was redacted in such a military-secure way, or whether black bars were simply placed over the text. I've reached out to a couple of news organizations offering my consulting help, but didn't get a nibble.
[0]: https://www.newyorker.com/tech/annals-of-technology/the-declassification-engine-reading-between-the-black-bars https://www.newyorker.com/tech/annals-of-technology/the-decl...
- seanmcdirmid 8y agoCould a recurrent neural network predict what the redacted text looked like similar to how it can predict sentences from starts by training over newspapers and Wikipedia? Probably not useful like the above work, but it would be entertaining like Google Dream was.
- haasted 8y ago> With guesses or context you can fill those in with high probability. In an extreme case, the redaction might be a name of let's say a member of Congress Something like this actually happened somewhere in the report. A name was at the end of a line, causing a part of it to break unto the next line. The few, black characters present on the second line lead readers to theorise that the name ends with “jr”.
- woozyolliew 8y agoMaybe Mueller figured out who killed JR?