08-17-2017, 12:35 AM
Breaking Audio CAPTCHAs
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Introduction
CAPTCHAs [1] are automated tests designed to tell computers and humans apart by
presenting users with a problem that humans can solve but current computer programs
cannot. Because CAPTCHAs can distinguish between humans and computers with high
probability, they are used for many different security applications: they prevent bots from
voting continuously in online polls, automatically registering for millions of spam email
accounts, automatically purchasing tickets to buy out an event, etc. Once a CAPTCHA is
broken (i.e., computer programs can successfully pass the test), bots can impersonate
humans and gain access to services that they should not. Therefore, it is important for
CAPTCHAs to be secure.
Literature review
To break the audio CAPTCHAs, we derive features from the CAPTCHA audio and use
several machine learning techniques to perform ASR on segments of the CAPTCHA. There
are many popular techniques for extracting features from speech. The three techniques we use
are mel-frequency cepstral coefficients (MFCC), perceptual linear prediction (PLP), and
relative spectral transform-PLP (RASTA-PLP). MFCC is one of the most popular speech
feature representations used. Similar to a fast Fourier transform (FFT), MFCC transforms an
audio file into frequency bands, but (unlike FFT) MFCC uses mel-frequency bands, which
are better for approximating the range of frequencies humans hear. PLP was designed to
extract speaker-independent features from speech [4]. Therefore, by using PLP and a variant
such as RASTA-PLP, we were able to train our classifiers to recognize letters and digits
independently of who spoke them. Since many different people recorded the digits used in
one of the types of audio CAPTCHAs we tested, PLP and RASTA-PLP were needed to
extract the features that were most useful for solving them.
Creation of training data
Since automated programs can attempt to pass a CAPTCHA repeatedly, a CAPTCHA is
essentially broken when a program can pass it more than a non-trivial fraction of the time;
e.g., a 5% pass rate is enough.
Our approach to breaking the audio CAPTCHAs began by first splitting the audio files into
segments of noise or words: for our experiments, the words were spoken letters or digits. We
used manual transcriptions of the audio CAPTCHAs to get information regarding the
location of each spoken word within the audio file. We were able to label our segments
accurately by using this information.
Support vector machine
To conduct digit recognition with SVM, we used the C++ implementations of libSVM [8]
version 2.85 with C-SMV and RBF kernel. First, all feature values are scaled to the range of
-1 to 1 as suggested by [8]. The scale parameters are stored so that test samples can be
scaled accordingly. Then, a single multiclass classifier is created for each set of features
using all the segments for a particular type of CAPTCHA. We use cross-validation and grid
search to discover the optimal slack penalty (C=32) and kernel parameter ( =0.011).