Completely Automated Public Turing tests to tell Computers and Humans Apart (CAPTCHA) serve as the primary defense mechanism against automated bot attacks on web services. This paper explores the structural weaknesses inherent in audio-based CAPTCHA challenges, with a specific focus on the MTcaptcha framework. While visual CAPTCHAs have been the primary target of previous bypass research, audio alternatives—mandated by accessibility standards (WCAG)—often present a weaker security barrier. We propose a methodology for bypassing MTcaptcha using a combination of modern signal processing and deep learning techniques, specifically leveraging Convolutional Neural Networks (CNNs) trained on spectrogram representations of audio signals. Our findings suggest that the entropy of current audio challenges is insufficient against contemporary Machine Learning (ML) models, necessitating a paradigm shift in CAPTCHA design toward behavioral analysis and hardware-based attestation.
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: Even if you bypass the CAPTCHA, aggressive hitting of a server can lead to IP blacklisting or legal action. Completely Automated Public Turing tests to tell Computers