Enhancing website security is crucial to combat malicious activities,and captcha(Completely Automated Public Turing tests to tell Computers and Humans Apart)has become a key method to distinguish humans from *** text-...
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Enhancing website security is crucial to combat malicious activities,and captcha(Completely Automated Public Turing tests to tell Computers and Humans Apart)has become a key method to distinguish humans from *** text-basedcaptchas are designed to challenge machines while remaining human-readable,recent advances in deep learning have enabled models to recognize them with remarkable *** this regard,we propose a novel two-layer visual attention framework for captcharecognition that builds on traditional attention mechanisms by incorporating Guided Visual Attention(GVA),which sharpens focus on relevant visual *** have specifically adapted the well-established image captioning task to address this *** approach utilizes the first-level attention module as guidance to the second-level attention component,incorporating two LSTM(Long Short-Term Memory)layers to enhance captcha *** extensive evaluation across four diverse datasets—Weibo,BoC(Bank of China),Gregwar,and captcha 0.3—shows the adaptability and efficacy of our *** approach demonstrated impressive performance,achieving an accuracy of 96.70%for BoC and 95.92%for *** results underscore the effectiveness of our method in accurately recognizing and processing captcha datasets,showcasing its robustness,reliability,and ability to handle varied challenges in captcharecognition.
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