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Attacking CAPTCHAs explained Ioan – Carol Plangu

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Whats a CAPTCHACompletelyAutomatedPublicTuring test to tellComputers andHumansApart

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Three attack methods Implementation attack Automated recognition Manual labor

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The implementation attackScenario 1 the image session id can be reused

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The implementation attackScenario 1 the image session id can be reused id Restricted Captcha page form

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The implementation attackScenario 2 the number of captcha tests is limited

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The implementation attackScenario 2 the number of captcha tests is limited we just need to solve them all and store them in a hash table

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The implementation attackScenario 3 hash of solution sent to client

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The implementation attackScenario 3 hash of solution sent to client rainbow tables :)

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Manual laborThere are two options:

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Pay a bunch of monkeys

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Or not... XXX Complete this captcha form to continue

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Automated recognitionWere going to actually reproduce a human response for the given question

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Can you understand my voice?

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The sound sample is usually generated

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Its hard to add noise to thegenerated speech without making it hard for the human

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But can you read?

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Sort of.....

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The most common approach Greedy optimization – reverse engineer everything Character segmentation OCR

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Possible security measures

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Possible security measures Funky background image

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Possible security measures Funky background image − usually can be removed with basic preprocessing

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Possible security measures Funky background image − usually can be removed with basic preprocessing Text distortions

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Possible security measures Funky background image − usually can be removed with basic preprocessing Text distortions − modern OCR techniques can beat it

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Possible security measures Funky background image − usually can be removed with basic preprocessing Text distortions − modern OCR techniques can beat it Anti segmentation measures

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Beating segmentation

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Beating segmentation  If a character signature can be extracted from only the vertical signature, character segmentation becomes trivialA Low-cost Attack on a Microsoft CAPTCHA - Jeff Yan, Ahmad Salah El AhmadSchool of Computing Science, Newcastle University, UK

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Beating segmentationWe can otherwise ignore it!

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Beating segmentationWe can otherwise ignore it!The following slides are about an experiment about this approach

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A Monte-Carlo experiment Note: for testing performance, the variance of the characters has been kept to a minimumf(x) → yx in binary( 0 - 2^3000 )y in 10^6

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Training: − Select one character image at random − Select N black spots − Sort the points for uniqueness − Subtract the first point from all others for position independence − Assign it a weight for each character using the following formula: matched characters count / sample size − Assign it a score (indicates classification quality) selected digit weight / (1 + other digit weights)

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Recognition: − Make a score map for all points − Select the most appropriate character for each column − Process the resulting string into a 6 digit string

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An equivalent model input layer linear hidden layer (feature layer) threshold layers softmax layer

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An equivalent model input layer OCR linear hidden layer (feature layer) without zero penalty == threshold layers No biases for the first layer (avoids the 2*binary - 1 effect) softmax layer

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Hacking the OCR: To negate the effect the biases, for each image we add random noise in the white areas This will greatly improve the recognition in a noisy image

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An more powerful model input layer Hacked OCR layer Score map output layer

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Questions?

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The demo source is hosted athttps://github.com/theshark08/howtobreakacaptcha01