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Breaking Audio CAPTCHAs - Carnegie Mellon University
https://www.cs.cmu.edu/~jdtam/Documents/Breaking_Audio_CAPTCHAs.pdf
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.
Breaking Audio CAPTCHAs
https://proceedings.neurips.cc/paper/2008/hash/12092a75caa75e4644fd2869f0b6c45a-Abstract.html
Our approach to breaking the audio CAP T C H A s 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 CAP T C H A s to get information regarding the location of each spoken word within the audio file.
Breaking audio CAPTCHAs | Proceedings of the 21st ...
https://dl.acm.org/doi/10.5555/2981780.2981983
Audio CAPTCHAs are generally composed of a set of words to be identified, layered on top of noise. We analyzed the security of current audio CAPTCHAs from popular Web sites by using AdaBoost, SVM, and k-NN, and achieved correct solutions for test samples with accuracy up to 71%. Such accuracy is enough to consider these CAPTCHAs broken.
Breaking audio CAPTCHAs - researchgate.net
https://www.researchgate.net/publication/221617978_Breaking_audio_CAPTCHAs
Similar to methods for breaking text-based CAPTCHAs, Tam et al. [74] showed how machine learning techniques can also be used to solve audio CAPTCHAs. In their approach, a …
"Breaking Audio Captcha using Machine Learning/Deep ...
https://scholarworks.sjsu.edu/etd_projects/741/
Whereas, audio captchas with high noise were relatively more secure with breaking accuracy of 85%. I also propose that adversarial example attacks can be used in favor of audio captcha, that is, adversarial example attacks can be used to defend audio captcha from attackers.
Reducing the Cost of Breaking Audio CAPTCHAs by …
https://www.ruhr-uni-bochum.de/lmi/darnstdt/captchas_icml2014.pdf
high success rates for breaking audio CAPTCHAs. Our experiments are conducted on two different data sets, a development and an evaluation set. The development set is given by the Aurora-5 speech corpus [4] that contains thou-sands of noisy digit sequences. The digit sequences constitute real speech recordings spoken by various speakers. The eval-
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