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| Subject: | Re: Defeating CAPTCHAs via Averaging |
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| Date: | Wed, 31 Jan 2007 00:55:41 +0100 |
I am not sure I understand how you propose to build an automatic system to attack it: If you can tell that two images contain the same number then it is very likely that you can recognize the numbers themselves (there are only 10 different digits).
Well if one of the stated conditions (being that the predominant distortion in the captcha is of a noise-like nature) then you won't need to find out if the numbers are identical or not. You will simply find the number, something you wouldn't be able to do when the distortion isn't noise-like. So when getting the same captcha several times and averaging out the noise-like distortion will not result in a number which OCR software can recognize then there can be a (programmatic) conclusion that either 1) the distortion wasn't noise-like, or 2) the numbers aren't identical in the repeteated gets.
So an automatic attack system would scan sites for captchas, try doing the averaging trick, probably find a lot of negatives, but find some positives.
OTOH, if you have a human in the loop, they can just use gimp to create the averaged figure images from a single image per figure, and then use these templates to calculate correlation in different places of a given challenge.
I don't think your understanding of averaging out noise is quite the same as mine (or the author's?). There's no 'template' with which you can filter out noise-like distortion. You need multiple different images. 'noise-like' means random, so as many values to the left of the average as to the right of the average of the noise. Averaging will make the result go near the average as the word implies, and make the noise 'disappear'.
Either way, when simple algoritms like averaging can decypher a captcha, then it's not really a captcha, is it? :)
Regards, Fred Leeflang
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