--- type: Article title: The Failure of Noise-Based Non-Continuous Audio Captchas description: "An S&P 2011 paper presenting a generic pipeline that breaks audio CAPTCHAs built from non-continuous speech, combining audio processing with machine learning. The authors report success against every popular scheme of the period, including Microsoft's and Yahoo's. Only the abstract is archived here; the paper and slides remain as PDFs on the publisher's CDN." resource: "https://elie.im/publication/the-failure-of-noise-based-non-continuous-audio-captchas" tags: [article, webseclist-reference, en, elie-net, auth-bypass, measurement-study, mitigation, owasp-a01-2021] generated: by: webseclist-refs/1 at: "2026-08-17T10:07:37+00:00" status: stable stale_after: 2027-08-17 sources: - id: original resource: "https://elie.im/publication/the-failure-of-noise-based-non-continuous-audio-captchas" title: The Failure of Noise-Based Non-Continuous Audio Captchas author: Elie Bursztein, Romain Bauxis, Hristo Paskov, Daniele Perito, Celine Fabry, John C. Mitchell also_at: - "https://cdn.elie.net/static/files/the-failure-of-noise-based-non-continuous-audio-captchas/the-failure-of-noise-based-non-continuous-audio-captchas-paper.pdf" - "https://cdn.elie.net/static/files/the-failure-of-noise-based-non-continuous-audio-captchas/the-failure-of-noise-based-non-continuous-audio-captchas-slides.pdf" authors: - Elie Bursztein - Romain Bauxis - Hristo Paskov - Daniele Perito - Celine Fabry - John C. 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The Failure of Noise-Based Non-Continuous Audio Captchas Elie Bursztein∗ Romain Beauxis† Hristo Paskov∗ Daniele Perito‡ Celine Fabry, John Mitchell∗ ∗ Stanford University, † Tulane University, ‡ INRIA {elie|hpaskov|jcm}@cs.stanford.edu, rbeauxis@tulane.edu, perito@inrialpes.fr, celine@celine.im Abstract—CAPTCHAs, which are automated tests intended the digit. When machine learning algorithms are trained to to distinguish humans from programs, are used on many overcome the distortions of an individual captcha scheme, web sites to prevent bot-based account creation and spam. they are far more effective than speech recognition systems To avoid imposing undue user friction, CAPTCHAs must be easy for humans and difficult for machines. However, [3], [26]. the scientific basis for successful CAPTCHA design is still In this paper, we describe a two-phase approach that is emerging. This paper examines the widely used class of sufficient to break modern audio captchas. One reason that audio CAPTCHAs based on distorting non-continuous speech audio captchas might be weaker than visual captchas stems with certain classes of noise and demonstrates that virtually from human physiology: the human visual system consumes all current schemes, including ones from Microsoft, Yahoo, and eBay, are easily broken. More generally, we describe a far larger portion of our brains than the human audio a set of fundamental techniques, packaged together in our processing system. In addition, modern signal processing Decaptcha system, that effectively defeat a wide class of audio and machine learning methods are fairly advanced. As a CAPTCHAs based on non-continuous speech. Decaptcha’s result, the difference between human and computer audio performance on actual observed and synthetic CAPTCHAs capabilities is likely significantly less than the difference indicates that such speech CAPTCHAs are inherently weak and, because of the importance of audio for various classes of between human and computer visual processing. users, alternative audio CAPTCHAs must be developed. While we believe our results demonstrate practical breaks, there is room for some debate on the success rate needed I. I NTRODUCTION to consider a captcha scheme ineffective in practice. In Many websites rely on Completely Automated Pub- many applications, a concerted attacker may attempt to lic Turing tests to tell Computers and Humans Apart set up fraudulent accounts using a large botnet (e.g., [16]). (CAPTCHA1 ) [18] to limit abuse in online services such as Since modern botnets may control millions of compromised account registration. These tests distinguish between humans machines [24], it is reasonable to expect that an attacker and automated processes by presenting the user with a task could easily afford to make one hundred attempts for every that is easy for humans but hard for computers. Designing desired fraudulent account. Therefore, a computer algorithm such tests, however, is becoming increasingly difficult that solves one captcha out of every one hundred attempts because of advances in machine learning. In particular, the would allow an attacker to set up enough fraudulent accounts widely used category of image based captchas have received to manipulate user behavior or achieve other ends on a target close scrutiny recently [17], [25], [30], [31]. site. A target 1% success rate is conservative relative to other While widely provided for accessibility reasons, audio studies, which hold that “automatic scripts should not be captchas have received substantially less scientific attention. more successful than 1 in 10,000” attempts [11]. In fact, we Virtually all current audio captchas on popular sites consist greatly surpass 1% in all but one case. of a sequence of spoken letters and/or digits that are distorted Contributions. We present Decaptcha, a two-phase audio with various kinds of noise. For simplicity, we will refer captcha solver that defeats modern audio captchas based to such non-continuous audio captchas simply as audio on non-continuous speech. The system is able to solve captchas in the remainder of the paper. Microsoft’s audio captchas with 49% success and Yahoo’s Almost a decade ago, Kochanski et al [15] investigated with 45% success, often achieving better accuracy than the security of audio captchas and developed a synthetic humans. This performance also comes at a low training benchmark for evaluating automatic solvers. This study, cost because Decaptcha requires 300 labeled captchas and which concludes that humans outperform speech recognition approximately 20 minutes of training time to defeat the systems when noise is added to spoken digits, has guided the hardest schemes. After training, tens of captchas can then design of modern audio captchas. Two later and independent be solved per minute using a single desktop computer. studies [27], [23] demonstrate that a two-phase segment- We also evaluate Decaptcha on a large-scale synthetic and-classify approach is sufficient to break older versions corpus. Our results indicate that non-continuous audio of Google and Yahoo audio captchas. Two-phase solvers captcha schemes built using current methods (without operate by first extracting portions of the captcha that contain semantic noise) are inherently insecure. As a result, we a digit and then using machine learning algorithms to identify suspect that it may not be possible to design secure audio 1 For readability, we will write captcha instead of CAPTCHA in the rest captchas that are usable by humans using current methods. of this paper. It is therefore important to explore alternative approaches. Root Mean Square (RMS). RMS measures the Decaptcha’s success stems from the following contributions: acoustic energyqof an audio signal and is defined as s2 +...s2 • Automated segmentation. We present a low-cost tech- RM S(S) = 0 n−1 . Values are reported on a n nique based on acoustic energy analysis that accurately logarithmic scale in units of decibels (dB). segments noisy audio. • Speech analysis. We test the performance of a variety of techniques for speech and signal analysis in the Signal to Noise Ratio (SNR). SNR measures the presence of various kinds of noise. relative energy between audio and noise signals as • Fully Automated Classification. We demonstrate the 10 log10 ( RM S signal RM S noise ). efficacy of a technique for automatic parameter tuning of the RLSC algorithm suggested by [21]. B. Signal Representations • Real World evaluation. We evaluate the performance of Decaptcha on a large corpus of real world Captchas Once each digit has been extracted, it is represented as a from Authorize, Ebay, Microsoft, Recaptcha, Yahoo temporal signal. This section discusses the transformations and Digg. that can be applied to this signal to improve digit recognition. • Synthetic evaluation We perform a large scale evalua- Discrete Fourier Transform (DFT). The DFT, also re- tion of the effects of different kinds of noise on captcha ferred to as the Fast Fourier Transform (FFT), of a signal security. The 4.2 million synthetic captchas used for decomposes it into a series of sinusoids. Formally, DF T (S) this study are generated by a method presented in [15], reparameterizes S into n complex coefficients f0 , . . . , fn−1 which is freely available.2 Pn−1 i where fi = u=0 sj e−2πu n . The fi quantify how much of the signal occurs as a sinusoid with frequency of ni and Outline. The remainder of the paper is organized as follows: therefore represent S in the frequency domain. The inverse In Section II we review the audio processing and machine DFT (IDFT) Pn−1 is computed using the following formula: learning techniques that are used in Decaptcha. In Section III i s0i = n1 u=0 fj e2πu n . we describe how Decaptcha is implemented and the design decisions behind each step. In Section IV we present the Cepstrum. The cepstrum [6] is computed as S → DF T → real captcha schemes that were evaluated, give Decaptcha’s magnitude → log → IDF T → magnitude where the performance on them, and discuss specifics. In Section V magnitude and log operations are applied element-wise to we present the synthetic captchas that were tested using an input sequence. The cepstrum gives a frequency analysis Decaptcha, give performance as a function of noise, and of the energy of the harmonics contained in the signal. discuss. Finally, we present related work in Section VI and conclude in Section VII. Spectro-Temporal Features (STF). STFs refer to a class of 2D representations that track how a unidimensional II. BACKGROUND transform (such as a DFT or cepstrum) changes over time. Given a unidimensional transform F , the STF of S computes Decaptcha can be split into three main components: a seg- F on intervals of length m, called frames, every k time steps. mentation stage that extracts spoken digits, a representation We will refer to the STF using a DFT or a cepstrum as the scheme for the extracted digits, and a classification stage TFR and TCR respectively. that recognizes each digit. While Decaptcha uses two active phases, the intermediate representation is an important part Two-Dimensional Cepstrum (TDC). Our last of a two-phase solver because of its impact on performance. representation scheme is another 2D variant of the Accordingly, the first three subsections of this section provide cepstrum that has proved useful for voice recognition [1], a high-level overview of the concepts used in each of the [10]. Given the TCR of S computed over d frames of length three components. We then discuss the metrics that are used m and stored in a m × d matrix X, the TDC computes the to measure Decaptcha’s performance. inverse DFT of each row of X. This depicts the frequencies with which cepstral coefficients change across frames. A. Segmentation An audio captcha is initially represented as a signal We have experimented with the following signal analysis S = s0 , . . . , sn−1 where each si denotes the amplitude techniques in an attempt to provide better noise filtering. of the signal at fixed time intervals. The spacing of these time intervals is called the sampling rate. Segmentation Blackman Window. Blackman windowing is a technique finds contiguous intervals of S that contain digits and as used to combat deleterious effects on the DFT caused little extraneous noise as possible. We briefly discuss useful by discontinuities that appear around the edges of each statistics for this process, referring the reader to [13] for a frame. In particular, a frame is weighted by a function more detailed discussion of concepts used in this section that drops to zero around its edges before computing and the next. the DFT. The weight of each sample is given by wi = 0.42 − 0.5 cos 2πi m + 0.08 cos 4πi m . 2 Those interested in the corpus should contact the authors. classifiers. The first classifier would be trained by labeling all pictures of cats as positive and all pictures of dogs and horses as negative. The second and third classifiers would be trained in a similar fashion. Given a new picture, we would, for example, label it a cat if and only if the first classifier gives it a higher score than the other two. Testing Error. Testing error measures how well a classifier generalizes and is defined as the expected frequency with which it will misclassify a new point. It is clear that the objective of both, binary and multi-category classification, is to come up with a decision rule that has minimal testing error. A reliable estimate of testing error can be obtained by computing a classifier’s error on a sample of points that were not used during training. Figure 1. Representations of a “four” digit Regularized Least Squares Classification (RLSC). RLSC is an adaptation of a regression algorithm to binary classifica- Mel Scale. The mel scale redistributes frequencies on tion [22]. It was introduced as a fast yet similarly performing a logarithmic scale that matches how humans perceive alternative to the Support Vector Machine (SVM), one of frequencies. Themel value of a frequency f is given by the most successful binary classifiers. In its simplest form, f m = 2595 log10 700 + 1 . We also use the mel scale to RLSC finds a hyperplane w ∈ Rd that separates its training shrink our data without impacting useful information by data into positive and negative classes by solving averaging intervals of mels. N 1X Figure 1 illustrates, in top-down left to right order, the w = min (yi − wT xi )2 + λwT w w∈Rd n original representation of the digit four, its DFT, Cepstrum, i=1 TFR, TCR, and TDC. The label of a new point x ∈ Rd is found by taking the C. Classification sign of wT x. It takes min(O(N 3 ), O(N d2 + d3 )) to solve for w. We can also train an entire OVA scheme consisting The classification stage of Decaptcha receives a digit of T classifiers in the time it takes to train a single binary represented in one of the aforementioned schemes and classifier. attempts to recognize it. This section provides an overview of the classification algorithm Decaptcha employs (see [22] for a thorough description). We begin with a high level Leave One Out (LOO). The LOO error of a binary classifier discussion of classification before moving on to the actual is another estimate of the testing error. It is found as the algorithm. average error the classifier makes on each point when it is Binary Classification. Given N labeled examples held out from the training set. Computing the LOO error (x1 , y1 ), . . . , (xn , yn ) with xi ∈ Rd and yi ∈ {−1, 1}, in general requires training N classifiers, but in the case of binary classification attempts to train a classifier, i.e. find a RLSC, it can be found at the cost of training a single binary decision rule, that accurately predicts the labels of unseen classifier. The LOO error of an OVA scheme is similarly points. It is customary to assume that a classifier outputs a found by holding out each point and checking whether the real number that is thresholded at some value – typically OVA scheme trained on N − 1 points predicts its label. zero – to determine a binary label. This multi-category LOO error can be found efficiently by computing the LOO error of each binary classifier in the OVA scheme. One Versus All (OVA). Multi-category classification ex- tends the binary paradigm by allowing labels to be any Parameter Tuning. The accuracy of RLSC critically relies integer in 1, . . . , T for a fixed number, T , of classes. The on a regularization parameter λ that keeps the classifier OVA scheme solves multi-category classification by training from overfitting or underfitting its training data. A good T separate binary classifiers. Classifier i is trained on labels value for λ can be found by trying a range of regularization that are positive for all training points labeled as class i parameters and selecting the one with lowest LOO error. and negative for all others. A new point is classified by It is possible to compute the LOO error of O(min(N, d)) running it through all T classifiers and taking the index of candidate values in the time it takes to solve a single binary the highest scoring one. For example, if we are classifying classifier. pictures of cats, dogs, and horses, we would train 3 binary -10 Captcha scraping -15 Magnitude (dB) -20 Sound processing Web Site -25 Discretized and segmented captcha -30 0 1 2 3 4 5 6 7 Captcha labels Answers Time (sec) Mechanical Turk users -10 Classifier -15 Magnitude (dB) -20 Figure 2. Overview of the System -25 -30 0 1 2 3 4 5 6 7 D. Metrics Time (sec) We conclude our overview with a discussion of the metrics Figure 3. RMS peak analysis that are used to evaluate the performance of a captcha solver. The most basic measure of performance is accuracy, the B. Segmentation fraction of captchas that are answered correctly. However, Segmentation determines which pieces of the captcha solvers may also select which captchas they respond to, in are likely to contain digits by looking at a subsampled which case we need more fine grained metrics. Coverage version of the signal. Given a window length l, the original is the fraction of captchas that a solver attempts to answer. signal is split into segments of length l and is subsampled by For Decaptcha, this is the number of captchas that were computing their RMS. A cut is extracted when a consecutive segmented correctly. Precision is the accuracy of the solver, sequence of segments computed only on the captchas it attempts to answer. This • lasts a minimal duration (0.4 sec by default). metric is equivalent to Decaptcha’s classification accuracy. • contains at least one segment with RMS higher than Finally, as will be discussed later, Decaptcha is a supervised a given level (−16 dB by default), referred to as the solver that requires a set of sample captchas to train on. A noise level. measure of a supervised algorithm’s efficiency is given by • ends with a segment whose RMS is below the noise the corpus size, the number of labeled captchas, it needs level and is a local minimum. to train on to obtain a specific accuracy. A lower value We determine appropriate values for the noise level and indicates a less secure captcha scheme because it requires window length for each captcha. Indeed, optimal parameters less training effort to break. differ even among captchas from the same scheme. These parameters are jointly optimized over a grid of values III. D ECAPTCHA between −10 to −30 dB for noise level and 0.1 to 0.5 This section describes Decaptcha and the design decisions sec for window length. The largest window size and noise behind it. We begin with an overview of the system and then level that produce a correct number of segments for the discuss its segmentation, representation, and classification given captcha scheme are selected. We prefer larger values components. because they give a clearer separation between noise and signal and avoid overly small cuts. Furthermore, joint optimization is necessary because the two parameters are A. Overview interrelated; noise level is a measure of energy that depends Decaptcha is a supervised algorithm that must be trained on the window length. on each captcha scheme. Training requires a set of captchas labeled with their answers. It outputs an automatic solver We chose this segmentation algorithm because it is and an estimate of Decaptcha’s expected performance on fast and does not modify the original signal. Traditional the scheme. Figure 2 depicts the interactions of Decaptcha’s methods[2] for noise analysis filter the frequency three processing stages during and after training. The representation of a captcha and therefor require computing segmentation stage is unsupervised, i.e. it is does not the DFT and IDFT of the entire signal. This method is undergo training, and it ”cuts” out the pieces of a captcha that computationally expensive for large-scale experiments and are likely to contain digits. Each cut is then converted to a leads to a potential loss of information when switching representation scheme that must be specified before training. between frequency and temporal representations. Finally, a (supervised) classifier is trained to recognize the digit in each cut. The next three sections detail each of these In order to understand the workings of our segmentation stages. approach, it is necessary to categorize the types of noise present in a captcha. Captchas usually feature a combination D. Classification of distortions present as low energy background noise and Decaptcha classifies digits using the RLSC algorithm in a medium energy intermediate noise. The simplest form an OVA scheme. Parameter tuning is automated via the of noise, constant noise, is a constant sound such as white method described in [21] and is performed by computing noise or perpetual buzzing. More complicated noise that the multi-category LOO of a range of regularization changes over time but has similar energy level, duration, parameters. We handle the classification of unidimensional and regularity is known as regular noise. Unrecognizable and two-dimensional representations differently. For speech that is repeated throughout the captcha falls into efficiency reasons, the TDC is handled by looking at this category. Finally, semantic noise consists of a signal the first 5,000 dimensions when they are extracted that has similar characteristics to a spoken digit but is not column-wise. In constrast, only the first 75 dimensions a useable digit. We consider music or vocal tracks to be of a unidimensional representation are used. A cut is semantic noise. represented by a 2850-dimensional vector consisting of Our RMS subsampling acts as a low-pass RMS filter these 75 dimensions and all pair-wise products between that eliminates constant and regular noises and only leaves them. This representation was chosen because it minimizes peaks that correspond to digits. In particular, constant the multi-category LOO for all of our real world captcha noise increases the RMS of each window uniformly and schemes. These experiments suggest that the first 75 cannot destroy or add peaks. Similarly, short, regular noise dimensions of the cepstrum contain all of the information retains digit peaks and impacts the RMS of each window necessary to identify a spoken digit and that neighboring approxmately uniformly for appropriate values of l. This cepstral coefficients are strongly correlated with each other. last assumption is true in audio captchas because regular noise must have an average duration that is much shorter Finally, STFs are handled differently because of than a digit for the captcha to be understandable by humans. misalignment. In general, cuts differ in length and the Figure 3 illustrates our RMS subsampling when the window actual frame in which each digit begins. We handle these length is too short (top) and when it is optimal (bottom). issues by classifying the contents of a window 30 frames in The bottom graph smooths out the signal but retains peaks length. This window length is chosen for efficiency reasons that correspond to the 10 digits present in the captcha. In because it fits an entire digit but is still small. A cut is contrast, an overly short window allows noise to create a classified by sliding the window along the time axis and jagged energy plot. selecting the digit which receives the highest vote from the OVA scheme at any time point. This approach effectively It is important to note that our segmentation technique selects the maximum response obtained from correlating is not robust to semantic noise. This kind of noise creates the cut with filters that look for each digit. artificial peaks in the subsampling procedure that differ from digits only on a semantic level. This weakness is tolerated RLSC is trained with an additional noise class that forces in Decaptcha because semantic noise is not common in each OVA classifier to learn a more robust decision rule. audio captchas. Moreover, semantic noise that approaches the This noise class is not used during the predictive phase. energy of spoken digits is confusing to humans and results Samples of digits are obtained from the first 30 frames of in frustrating captchas. This phenomenon is demonstrated each cut while samples of noise are taken from the last 30. in Recaptcha captchas which have a high error rate among This rule is used because of its simplicity and experimental humans. evidence that indicates that digits are more likely to occur at the beginning of the cut. It is clear that some noise C. Representation samples will contain fragments of digits, and vice versa, Decaptcha can represent cuts in any of the schemes but RLSC’s robustness to noise allows it to learn when described in section II-B. A single representation scheme is some training data is incorrect. chosen before training and must be used thereafter. Based on our experiments, the default representation is the cepstrum. We chose the RLSC algorithm over its more popular We chose each of our representation schemes because counterpart, the SVM, because of its performance and of their popularity and efficacy in analyzing speech. In efficiency. Decaptcha requires a classifier that, in addition to particular, the cepstrum is an experimentally validated performing well, can be trained quickly and automatically for technique that is useful for speech analysis because it multi-category classification. SVM’s are problematic in these separates low-frequency periodic excitation from the vocal respects because T separate classifiers must be computed cords from the formant filtering of the vocal tract [19]. when using an OVA scheme with T classes. Moreover, Similarly, STFs are effective in speech recognition systems there is no efficient way to compute the LOO error of and physiological studies suggest that they may be used an SVM, so automatic parameter tuning is very costly. In in the brain [14]. Finally, the TDC has been applied contrast, RLSC uses dynamic programming to train an OVA successfully to recognizing Mandarin and slovak digits in scheme and tune necessary parameters in the time it takes the presence of noise, [20] , [10] respectively. to train a single binary classifier with a fixed regularization Figure 4. Authorize Captcha Figure 5. Digg Captcha parameter. These properties can make RLSC orders of magnitude faster to train than an SVM [21]. This efficiency is noticeable in Decaptcha; it takes 2 minutes (5 minutes) to train on thousands of captchas with a unidimensional (two-dimensional) representation, respectively. IV. C OMMERCIAL C APTCHAS This section describes the commercial captchas we used to validate Decaptcha as well as our testing methodology and results. We tested audio captchas from Authorize, Digg, eBay, Microsoft, Recaptcha, and Yahoo. We were unable to test Google’s captchas because of difficulties we encountered obtaining reliable annotations; they are so difficult for humans that they are ineffective as captchas. Figure 6. Ebay Captcha A. Corpus description Authorize. Audio captchas on authorize.net consist of five letter J shows patterns similar to those of the letter J in letters or digits spoken aloud by a female voice. The voice the Authorize captcha (see figure 10). clearly articulates each character and there is minimal distortion. The waveform and spectrogram presented in eBay. Audio captchas on ebay.com consist of six digits Figure 4 show a portion of a captcha containing the spoken by a different speaker and in a different setting with digits/letters K, J, 5 and H. A long pause appears between regular background noise. The waveform and spectrogram spoken characters and vowels are clearly articulated. The presented in Figure 6 show part of a captcha containing the letters K and H, which are fricative consonants, show some digits 9, 5, 7 and 6. The digits in these captchas are delivered harmonic patterns in the spectrogram while the letter J has much faster than those of authorize.net or digg.com. The almost no harmonic patterns. waveform shows the variability of the various digits due to different speakers and different background noise levels, Digg. Audio captchas on digg.com consist of five letters while the spectrogram shows that the vowels are short and spoken aloud by a female voice. There is random white relatively unobscured by noise. noise in the background and sometimes an empty, but louder, segment is played between letters. The waveform Microsoft. Audio captchas from live.com consist of ten and spectrogram presented in Figure 5 show a portion of a digits spoken by different speakers over a low quality captcha containing the letters J, A and K. The overall brown recording. There is a regular background noise consisting color of the spectrogram shows the heavy constant noise of several simultaneous conversations. The waveform and that obscures vowels but still maintains some characteristic spectrogram presented in Figure 7 show a portion of a patterns of the letters. These patterns cannot be completely captcha containing the digits 2, 9, 0 and 0. Like the masked by the white noise since they are necessary for eBay audio captchas, these digits are spoken very quickly. human recognition. Interestingly, the spectrogram of the While all of the high amplitude sections of the waveform Scheme Authorize Digg eBay Microsoft Recaptcha Yahoo Length 5 5 6 10 8 7 Type of voice Female Female Various Various Various Child Background Noise None Constant (random) Constant (random) Constant (random) Constant (random) None Intermediate noise None None Regular (speech) Regular (speech) Regular (speech) Regular (speech) Charset 0-9a-z a-z 0-9 0-9 0-9 0-9 Avg. duration 5.0 6.8 4.4 7.1 25.3 18.0 Sample rate 8000 8000 8000 8000 8000 8000 22050 Beep no no no no no yes Table I C OMMERCIAL AUDIO C APTCHA F EATURE D ESCRIPTION Figure 7. Microsoft Captcha Figure 9. Yahoo Captcha vocal noises that look like digits in the waveform. Apart from the presence of semantic noise, Recaptcha captchas are similar to live.com captchas, but the digits are delivered much more slowly. The waveform and spectrogram presented in Figure 8 show a portion of a captcha containing the digits 1, 7, 3 and 5. As will be discussed in 10 the five digit from this captcha shows similar harmonic patterns the five digit from Authorize and eBay captchas. Yahoo. Audio captchas from yahoo.com consist of three beeps followed by seven digits spoken by a child. The captcha is obscured with other childrens’ voices in the background. The waveform and spectrogram presented in Figure 9 show a portion of a captcha containing the digits 1, 7 and 6. The digits are the largest amplitude sections in the Figure 8. Recaptcha Captcha waveform and the spectrogram shows that the background voices do not confuse the patterns of the digits. This spectrogram shows different patterns than the spectrograms correspond to actual digits, the spectrogram shows that of the other captchas because of the use of a child’s voice. It the vowels are somewhat obscured by background noise. seems that the patterns induced by a child are much clearer Interestingly, the two 0 digits show very similar patterns, than the patterns of an adult’s voice. This makes digits easier but this pattern is not easily distinguished from the pattern to recognize even though the noise in Yahoo’s captchas has observed for the 9 digit. more energy than the noise in other captchas. Recaptcha. Audio captchas from recaptcha.net consist of Comparison. Figure 10 illustrates some differences between eight digits spoken by different speakers. Distortions include commercial captcha schemes. The first line presents the background conversations and approximately two semantic TFR of the digit five from Authorize, eBay, and Recaptcha 100 Authorize 5: 90 Digg Ebay MSLive 80 Recaptcha Yahoo Per−Captcha Precision (%) 70 Authorize eBay Recaptcha 60 50 J: 40 30 20 Authorize Digg 10 0 2 3 4 Figure 10. The TFR of the same digit/letter for different captcha schemes 10 10 10 Corpus Size (in Digits) Figure 11. Per-captcha precision as a function of corpus size using the captchas, respectively. All three digits show similar patterns, cepstrum although the overall shape is different because of different speakers. The second line presents the TFR of the letter J 0 from Authorize and Digg captchas, respectively. Similar 3 7 2 9 4 1 patterns can also be observed: both images show few -10 0 N 5 N harmonic patterns because the letter J is a consonant with very few harmonic components. -20 -30 B. Labeling DB We used Amazon Mechanical Turk to label captchas -40 scraped from the aformentioned commercial schemes. We considered a captcha labeled if three different Turkers -50 independently agreed on the sequence of digits it contains. -60 This method allowed us to reuse some labels obtained during a previous study [4]. Nonetheless, we had to label -70 10,000 new captchas from each of Microsoft, Recaptcha, 0 20 40 60 80 100 120 140 160 180 200 Time in seconds and Yahoo because of label disagreement in our old corpus. Only approximately 10% of these new captchas satisfied Figure 12. A standard Recaptcha captcha with the peaks annotated our label requirement. Obtaining reliable annotations for Microsoft, Recaptcha, and Yahoo captchas turned out to be more difficult than one third of Microsoft’s captchas were consistently labeled expected: three or more individuals agreed on the same incorrectly. incorrect labels for some captchas. These incorrect labels were tolerated when training the RLSC algorithm because of C. Results its robustness to noise. However, we were forced to manually Decaptcha’s performance on commercial captcha schemes and accurately annotate 200 captchas from these schemes is presented in Table II and Figure 11. Figure 11 depicts for our testing sets because incorrect labels drastically affect the impact of the corpus size, measured in number of the measured testing error. To see its impact on the per digit labeled digits, on Decaptcha’s per-captcha precision using classification rate, suppose that a classifier has a precision the cepstrum. Table II is finer grained and it presents the of 0 ≤ p ≤ 1 and w fraction of our testing set is mislabeled. coverage, per-digit precision, and per-captcha precision of The testing precision that we measure is each captcha scheme for all possible signal representations. p(1 − w) + (1 − p)w Our best per-captcha accuracies are highlighted in bold and q= are always the unidimensional cepstrum. The per-captcha 10 precision of Decaptcha is 89% for Authorize, 41% for Digg, which is given by the likelihood of successfully labeling 82% for eBay, 48.9% for Microsoft, 45.45% for Yahoo and, a sample whose label is correct and mislabeling one that 1.5% for Recaptcha. We improve our previous work’s [3] was mislabeled with the same digit, assuming ten digits. 10q−w result on eBay from 75% up to 82%. If we solve for p, we get p = 10−11w which leads to an estimate of 60% precision when our real precision is 89% Recaptcha. Precision is particularly low on the Recaptcha in the case of Microsoft’s captchas. This implies that almost scheme because it uses semantic vocal noise. This noise has FFT Cepstrum Cepstrum+Mel TFR TFR+Mel TCR TDC Scheme Len Coverage Digit Captcha Digit Captcha Digit Captcha Digit Captcha Digit Captcha Digit Captcha Digit Captcha Authorize 5 100 93.73 80.39 96.08 87.25 97.06 89.22 92.55 77.45 91.76 71.57 83.14 34.31 97.25 88.24 Digg 5 100 71.08 32.07 76.77 40.84 76.61 41.04 62.15 35.66 74.66 36.65 70.96 27.69 72.19 31.08 eBay 6 85.60 81.58 44.36 92.48 82.88 92.61 80.93 81.84 47.08 81.91 44.36 45.40 0.78 90.60 75.88 Microsoft 10 80.60 76.57 14.69 89.58 48.95 89.30 47.55 88.95 46.85 86.99 41.26 84.48 28.67 87.20 42.66 Recaptcha 8 99.90 26.58 0.00 40.47 1.52 37.44 1.52 38.45 0.00 38.26 0.00 24.62 0.00 30.30 0.00 Yahoo 7 99.10 33.77 0.00 74.71 45.45 68.13 30.30 66.03 22.22 61.74 20.20 38.93 1.35 62.01 17.51 Table II D ECAPTCHA’ S C OVERAGE , P ER -D IGIT P RECISION , AND P ER -C APTCHA P RECISION AS A F UNCTION OF THE R EPRESENTATION S CHEME the same RMS, harmonic content, and overall characteristics is needed to provide a sufficient number of samples of each as a regular digit. These properties confuse our segmentation character. Similarly, we expect a lower rate of increase in algorithm because the only distinction between vocal noise precision, as a function of corpus size, in schemes with more and proper digits is in their meaning; it is the classifier’s job characters. This is shown in Digg’s precision curve: the rate to distinguish between the two. As evidenced by the large at which precision increases is considerably lower than the proportion of mislabeled captchas, humans are also more rate at which the precision of the 10-digit schemes increase. error prone in the presence of Recaptcha’s semantic vocal Nonetheless, the high initial precision of both Authorize and noise. These two factors make it difficult to acquire a good Digg indicates that the distortions present in these schemes training set and lead to poor segmentation during testing. are easily handled by Decaptcha. Performance estimation. A conservative estimate of De- It is interesting to observe that corpus size serves as an captcha’s precision on a specific scheme can be obtained by effective measure of captcha difficulty that coincides with raising the per-digit precision to the number of digits con- human observations. In particular Figure 11 allows us to tained in each captcha. This formula, however, consistently rank the schemes in terms of difficulty as Recaptcha being underestimates the per-captcha precision because it assumes the hardest, followed by a tie between Microsoft and Yahoo. that the probabilities of erring on the first, second, etc. digit Authorize is the easiest scheme, followed by eBay and then of a captcha are independent. To illustrate the problem with Digg. A similar ranking was observed from the percentage this assumption, suppose that the segmenter skips the first of captchas that were correctly labeled on Amazon Turk. digit of a captcha labeled ”1234”. During testing, the 2 will Impact of signal representation. The precision of each be labeled a 1, the 3 will be labeled a 2, and so on. This representation technique relative to the best performing one label misalignment ensures that each digit is counted as varies dramatically depending on the captcha scheme. For a mistake, irrespective of whether the classifier recognizes instance, the TDC is nearly optimal with Authorize, but it correctly. A second way in which digit misclassification it almost three times worse than the cepstrum on Yahoo probabilities are not independent is that a mistake on the first captchas. Overall, our results show that cepstrum is the best digit of a captcha is likely to be indicative of a particularly representation scheme for commercial captchas. However, noisy or otherwise difficult captcha. In this case, we are we will show in the next section that the TFR is better suited likely to make mistakes on the remaining digits. Conversely, when the SNR is low. misclassified digits are likely to belong to the same captcha, so per-captcha precision is higher than what it is estimated Classifier confusion. Figure 13 shows Decaptcha’s con- to be. fusion matrices on the Microsoft, Recaptcha, and Yahoo Training corpus size. Figure 11 shows the relationship schemes using the cepstrum. Note that we use an exponential between training corpus size and per-captcha success rate. scale because it leads to the best contrast. A confusion matrix For Authorize and eBay, Decaptcha achieves maximal depicts the conditional probability that a digit is classified as precision early, at approximately 200 and 1000 digits, a y given that it is actually an x, for all pairs of digits x, y. respectively. The other captcha schemes continue to benefit In general, such a matrix will not be symmetric. Microsoft’s from additional training data, although there are diminishing matrix indicates that the digits 9, 6, and 3 are often mistaken returns because the x-axis is on a logarithmic scale. If we for the digits 5, 2, and 2, respectively. These three pairs are account for the number of digits contained in each captcha, among the most frequently confused digits for Recaptcha a reasonable initial training corpus size requires between as well. The Recaptcha confusion matrix also reflects our 100 and 300 labeled captchas. The expected precision of overall higher error rates on the Recaptcha scheme. Finally, Decaptcha after training can then be used to decide whether Yahoo’s confusion matrix significantly differs from the other to add additional data. two and has a nearly uniform error distribution. This last Note that both Digg and Authorize use letters and digits property was observed because all off-diagonal elements for a total of 36 possible characters whereas the other become white when we subtract the mean from the original schemes only use 10 digits. The more varied a scheme is, confusion matrix. It will be interesting to investigate how the larger a corpus is needed to obtain the same number of confusion patterns change as a function of the sampling rate, examples per character. Obviously, therefore, a larger corpus voice, and distortions used. Figure 13. Decaptcha’s Confusion Matrices on Microsoft, Recaptcha, and Yahoo Schemes Using Cepstrum 100 V. S YNTHETIC E VALUATION 95 This section reports Decaptcha’s performance on a syn- thetic corpus that was generated following methodology 90 introduced by [15]. The synthetic corpus uses nine types 85 of noise described in Table III. We measured Decaptcha’s Coverage (%) performance on 2000 captchas for each noise type at SNRs 80 ranging from −5 to 30 dB. Each captcha is composed of six 75 spoken digits, spaced randomly between 0.5 and 4 seconds. 70 Familly Name Description White White Gaussian noise. 65 Constant Noise Sine waves at 700 Hz, buzz, echo, nina, pow buzz chopin, disintegrator, lofi, rnoise 2100 Hz and 3500 Hz. 60 white 10 ms bursts of white gregorian pow Gaussian noise repeated 55 every 100 ms. 30 25 20 15 10 5 0 −5 SNR (dB) Every 100 ms, a section of the signal is replaced rnoise Figure 14. Coverage as a Function of Noise by white noise of the same RMS amplitude. Regular noise Add distortion, cracks, bandwidth limiting and lofi cepstrum representations is presented in Figures 15 and 16, compression. Simulates old audio equipment. respectively. As expected, precision is directly related to The signal starts SNR. For readability purposes, we have collapsed all of the echo to echo at 0.6, 1.32, and 1.92 seconds. figures by combining similar curves. Amplifies random half- cycles of the signal by Constant noise. Precision drops most drastically for con- disintegrator a multiplier. Simulates stant noises once the SNR is below 5. Indeed, as the SNR a bad audio channel. Chopin Polonaise for gets close to zero, constant noises mask any spoken digits chopin and therefore make the captcha unintelligible. Decaptcha Piano No. 6, Op. 53. Semantic noise gregorian Gregorian chant. responds to such noise in a similar way as humans so “Just in time“ by nina Nina Simone. constant noise should only be used as background noise with a low RMS. Table III D ESCRIPTION OF THE N OISES U SED IN O UR S YNTHETIC C ORPUS Regular noise. The worst-measured precision of 64% is achieved on the pow noises. Otherwise, Decaptcha has a precision above 80% at all SNRs. We believe that the Performance. Decaptcha’s coverage of the synthetic corpus pow noise leads to poor precision because it confuses the is depicted in Figure 14. Our overall coverage is between segmentation algorithm when the SNR is low. Nonetheless, 80 and 90%, even for SNRs of −5dB. The only exceptions Decaptcha handles regular noise remarkably well, even occur with white and gregorian noise, which achieve a at low SNRs, which may suggest that computers can coverage of 60% at low SNRs. Precision using TFR and outperform humans with this type of noise. 100 example in March 2008, a method to break 60% of MSN 90 visual captchas was disclosed [29] and more recently an 80 attack against the recaptcha captcha was demonstrated at the Defcon[9]. Using machine learning to break captchas Per−Captcha Precision (%) 70 applies to almost every kind of captcha and in 2008, 60 Golle [8] successfully used machine learning attacks to 50 break the Microsoft picture based scheme Assira. 40 30 white VII. C ONCLUSION buzz 20 gregorian Decaptcha’s performance on commercially available audio nina chopin captchas indicates that they are vulnerable to machine 10 pow echo, lofi, rnoise, disintegrator learning based attacks. In almost all cases, we achieve 0 20 15 10 5 0 −5 accuracies that are significantly above the 1% threshold for SNR (dB) a scheme to be considered broken. Compared with human studies done in [4], Decaptcha’s accuracy rivals that of Figure 15. Precision of the TFR as a Function of Noise crowdsourcing attacks. Morever, our system does not require 100 specialized knowledge or hardware; its simple two-phase design makes it fast and easy to train on a desktop computer. 90 As such, automatic solvers are a credible threat and measures 80 must be taken to strengthen existing audio captchas. Our experiments with commercial and synthetic captchas Per−Captcha Precision (%) 70 indicate that the present methodology for building audio 60 captchas may not be rectifiable. Besides Recaptcha, all of the 50 commercial schemes we tested use combinations of constant 40 and regular noise as distortions. Based on the difficulties we had with obtaining reliable annotations, human accuracy 30 plummets when such distortions contribute significantly to white 20 pow the signal. On the other hand, Decaptcha’s performance on 10 gregorian nina, chopin our synthetic corpus indicates that automated solvers can disintegrator, echo, rnoise, buzz, lofi handle such noise, even at low SNRs. All in all, computers 0 30 25 20 15 10 5 0 −5 may actually be more resilient than humans to constant and SNR (dB) regular noise so any schemes that rely on these distortions Figure 16. Precision of the Cepstrum as a Function of Noise will be inherently insecure. Our results also pinpoint an inherent weakness of two- phase machine learning attacks that may be exploited, at Semantic noise. As expected from Decaptcha’s low preci- least temporarily. As evidenced by Decaptcha’s difficulties sion on Recaptcha, the nina, gregorian and chopin noises with Recaptcha, semantic noise hinders the segmentation produce the most robust captchas. Unlike constant noise, stage by introducing noise that can be confused with a digit. humans are well equipped to handle semantic noise, even Architectures that successfully overcome such distortions at low SNRs, because we can select which voice to listen require a monolithic design that blends together classification to. Furthermore, semantic noise consistently leads to lower and segmentation to endow the segmentation algorithm with precision than regular noise, especially at low SNRs. This semantic understanding. These designs are more difficult noise is therefore the least harmful to human understanding to realize than the simple two-phase approach and have at levels that hinder Decaptcha’s performance. received little attention. We therefore recommend that future designs for audio captchas investigate the use of semantic The impact of sound representation. A final takeaway noise. from this evaluation is that the TFR representation gives better results than the cepstrum when dealing with constant Future directions. We plan to extend our work in two noise at low SNRs. directions. First, we would like to modify Decaptcha to handle audio captchas that contain spoken words. It is VI. F URTHER R ELATED W ORK important to understand whether such “continuous” designs The first discussion of the captcha idea appears in [18], lead to more secure captchas. Secondly, we would like to though the term CAPTCHA was coined later in [28]. investigate a series of design principles that may lead to Text/image based captchas have been studied extensively more secure captchas. 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