TY - GEN
T1 - Solving Google's continuous audio CAPTCHA with HMM-based automatic speech recognition
AU - Sano, Shotaro
AU - Otsuka, Takuma
AU - Okuno, Hiroshi G.
PY - 2013
Y1 - 2013
N2 - CAPTCHAs play critical roles in maintaining the security of various Web services by distinguishing humans from automated programs and preventing Web services from being abused. CAPTCHAs are designed to block automated programs by presenting questions that are easy for humans but difficult for computers, e.g., recognition of visual digits or audio utterances. Recent audio CAPTCHAs, such as Google's audio reCAPTCHA, have presented overlapping and distorted target voices with stationary background noise. We investigate the security of overlapping audio CAPTCHAs by developing an audio reCAPTCHA solver. Our solver is constructed based on speech recognition techniques using hidden Markov models (HMMs). It is implemented by using an off-the-shelf library HMM Toolkit. Our experiments revealed vulnerabilities in the current version of audio reCAPTCHA with the solver cracking 52% of the questions. We further explain that background stationary noise did not contribute to enhance security against our solver.
AB - CAPTCHAs play critical roles in maintaining the security of various Web services by distinguishing humans from automated programs and preventing Web services from being abused. CAPTCHAs are designed to block automated programs by presenting questions that are easy for humans but difficult for computers, e.g., recognition of visual digits or audio utterances. Recent audio CAPTCHAs, such as Google's audio reCAPTCHA, have presented overlapping and distorted target voices with stationary background noise. We investigate the security of overlapping audio CAPTCHAs by developing an audio reCAPTCHA solver. Our solver is constructed based on speech recognition techniques using hidden Markov models (HMMs). It is implemented by using an off-the-shelf library HMM Toolkit. Our experiments revealed vulnerabilities in the current version of audio reCAPTCHA with the solver cracking 52% of the questions. We further explain that background stationary noise did not contribute to enhance security against our solver.
KW - audio CAPTCHA
KW - automatic speech recognition
KW - hidden Marcov model
KW - human interaction proof
KW - reCAPTCHA
UR - http://www.scopus.com/inward/record.url?scp=84891904580&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84891904580&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-41383-4_3
DO - 10.1007/978-3-642-41383-4_3
M3 - Conference contribution
AN - SCOPUS:84891904580
SN - 9783642413827
VL - 8231 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 36
EP - 52
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 8th International Workshop on Security, IWSEC 2013
Y2 - 18 November 2013 through 20 November 2013
ER -