Audio CaptCha
Transcript of Audio CaptCha
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N IMPR VED
AUDIO
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Contents
What are captchas? Problem with current audio captchas.Testing of current captchas.
Categories of audio Captcha. Algorithm used and its details. Need for audio reCaptcha. Applications. Pitfalls. Conclusion.
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WHAT ARE CAPTCHAS?
CAPTCHAs are tests generated by computers and
generally passable by humans but not current computer
programs.
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THE PROBLEM WITH CURRENT AUDIO
CAPTCHAS
In some cases the human passing rate is only 70%!
To make the CAPTCHAs secure, noise was injected
into the audio files making it harder forboth
computers and humans to pass.
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HOW DID WE TEST THE CURRENT AUDIO
CAPTCHAs?
Selected three different types of audio CAPTCHAs:
google, reCAPTCHA, and digg
Collected 1000 CAPTCHAs per type of audio
CAPTCHA to use for training and testing Created an ASR system using machine learning
techniques
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Three categories of audiocaptcha
reCAPTCHA audio captcha - multiplevoices, digits and background noisethat is backwards speech
Google audio captcha- digits, singlevoice, backwards speech
Digg audio captcha- digits andletters, static/water for noise
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THE ALGORITHM
Given the .wav file of an audio CAPTCHA
Segmentation - selecting portions of the audio
which most likely are digits/letters
Recognition
Extract features from the segment
Classify segment as digit/letter or noise and
output the label
Stop once a maximum number of segments are
classified
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ALGORITHM DETAILS - SEGMENTATION
CAPTCHAs were manually labeled and segmented.
We created training segments using this information.
For testing, we chose the highest energy peaks in the
test CAPTCHA and selected fixed size segmentsroughly centered at the peaks.
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QuickTime and adecompressor
are needed to see this picture.
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ALGORITHM DETAILS - FEATURES
We used three popular techniques for extracting
features from speech to derive 5 sets of features from
the audio.
Mel-frequency cepstral coefficients (MFCC) Perceptual linear prediction (PLP)
Relative spectral transform with PLP (RASTA-PLP)
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ALGORITHM DETAILS - AdaBoost
Used decision stumps for weak classifiers
For each type of audio CAPTCHA we created enough
classifiers to label a segment as a digit, letter, or noise.
Created 11 to 37 classifiers
Each classifier returns a value which represents its
confidence that the segment should be labeled as digit
letter or noise.
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ALGORITHM DETAILS - SVM
Created a single multiclass classifier using all the
training segments (from 900 CAPTCHAs)
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ALGORITHM DETAILS - k-NN
Created 5 classifiers corresponding to each of the
feature sets
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THE ALGORITHM
Input: Audio CAPTCHA as an audio file
Segmentation
Find the highest energy peak, and extract a fixed
size segment centered at that peak
Recognition
Extract features from segment
Give segment to classifier and obtain label
Stop extracting segments once all segments have been
labeled or a max solution size is reached.
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ANALYSIS OF CURRENT AUDIO
CAPTCHAs
Using three machine
learning techniques to
perform ASR on the
CAPTCHAs AdaBoost
Support Vector
Machines (SVM)
k-Nearest Neighbor
(k-NN)
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%
GooglereCAPTCHA Digg
Exact Match Rate
AdaBoost
SVM
k-NN
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THE GOAL
Make a secure audio CAPTCHA which will be easier
for a human to pass and harder for a computer to pass.
Equate solving a CAPTCHA with doing some useful
work. In other words, create an audio reCAPTCHA.
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WHAT IS reCAPTCHA?
reCAPTCHA helps digitize text on which OCR fails
by using the text as its CAPTCHA.
Since millions of people solve CAPTCHAs each day,
millions of words get digitized each day!
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THE AUDIO RECAPTCHA
Takes advantage of the human ability to understand
words through context.
Will help transcribe digital audio on which ASR
systems fail. The audio being used was originally recorded with the
intention that it should be easily understood by
humans.
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Applications
Preventing Comment Spam inBlogs.
Protecting WebsiteRegistration.
Protecting Email Addresses FromScrapers.
Online Polls
Preventing Dictionary Attacks.
Worms and Spam. 20
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ANALYSIS OF SECURITY
Speaker independent recognition is difficult.
Open vocabularies make it even more difficult for
ASR systems
AM broadcasts and .mp3 compression cause the lossof important data needed for automatic analysis
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CONCLUSION
CAPTCHAs need to be more accessible, yet remain
secure and not too difficult for humans.
Deploy audio reCAPTCHA through reCAPTCHA site.
Help make knowledge captured in audio available intext form
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Thank
you
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