Farsi CAPTCHA Recognition Using Attention-Based Convolutional Neural Network

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Hajyan Matine; Hosseni Alireza; Toosi Ramin; Akhaee Mohammad Ali

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2023 | Issue:9

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Title

Farsi CAPTCHA Recognition Using Attention-Based Convolutional Neural Network

Author(s)

Hajyan Matine | Hosseni Alireza | Toosi Ramin | Akhaee Mohammad Ali

Keywords

CAPTCHA Solver

Deep Learning

Computer Vision

Convolutional Neural Networks

Thin Plate Spline

Attention

Abstract

Getting around CAPTCHAs is essential for stopping fraudulent online activity. The creation of efficient CAPTCHA-breaking algorithms in the context of Persian can help safeguard Farsi-speaking users from a variety of online dangers and enhance their overall online experience. This study offers a novel method for recognizing Persian CAPTCHAs, which was developed and tested on a large and distinctive dataset. Our approach to Farsi CAPTCHA recognition leverages Deep Learning models, specifically a combination of the TPS-Resnet-BiLSTM-ATTN model, which surpasses other approaches and breaks Farsi CAPTCHAs with the highest possible accuracy. We have achieved amazing results with promising implications for boosting the security and usability of many online services that depend on CAPTCHA authentication by delving deeply into the impact of Attention modules on CAPTCHA recognition.

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APA: Copy

Hajyan, Matine, Hosseni, Alireza, Toosi, Ramin, & Akhaee, Mohammad Ali. (). . . SID. https://sid.ir/paper/1047268/en

Vancouver: Copy

Hajyan Matine, Hosseni Alireza, Toosi Ramin, Akhaee Mohammad Ali. . . Available from: https://sid.ir/paper/1047268/en

IEEE: Copy

Matine Hajyan, Alireza Hosseni, Ramin Toosi, and Mohammad Ali Akhaee, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1047268/en

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