# Enhancing Security of Online Interfaces: Adversarial Handwritten Arabic CAPTCHAs Generation
## 6 Conclusions and Future Work  
The aim of the study was to generate adversarial Arabic handwritten CAPTCHAs and evaluate the security and usability of different adversarial perturbation techniques for human readable CAPTCHAs. Five techniques, CTC, EOT, IAN, JSMA, and SGTCS, were implemented to distort Arabic handwritten words. Both automated recognition testing and human participant evaluations were conducted. Results showed JSMA achieved the optimal equilibrium of high protection and acceptable usability. Meaningful words enhanced performance in human recognition at 90.6%, while unpredictability in meaningless words aided security versus bots. The results provide direction for generation techniques and setting baselines, and focus on important tasks like perturbation calibration. Overall, the study advanced the development of Arabic handwritten CAPTCHAs that will be more secure and readable in the future.  
Potential directions for future work include combining perturbation techniques like JSMA and SGTCS into hybrid approaches to evaluate how they may enhance security against both third-party humans and machines. Additionally, optimizing the clarity of the handwritten fonts used could further improve the balance of usability and security.  
Conceptualization, S.A.A.; Methodology, G.A.; Software, G.A.; Validation, S.A.A.; Formal analysis, S.A.A.; Investigation, G.A.; Resources, G.A.; Data curation, G.A.; Writing--original draft, G.A.; Writing--review & editing, G.A. and S.A.A.; Visualization, G.A.; Supervision, S.A.A. All authors have read and agreed to the published version of the manuscript.  
The Researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support (QU-APC-2025).  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Technique** & **Text Type** &
\begin{tabular}{c} **Security Evaluation** \\ **Type** \\ \end{tabular} & **Security** & **Usability** \\ \hline
[35] & OCR & Meaningful words & OCR & \
\begin{tabular}{c} 51\% difference in segments \\ \end{tabular} &
\begin{tabular}{c} More than 88\% of accuracy \\ \end{tabular} \\ \hline
[34] & \
\begin{tabular}{c} Segmentation- \\ validation \\ \end{tabular} & Meaningful words & OCR &
\begin{tabular}{c} 95.5\%in \\ segmentation \\ attacks, 92.1\% in \\ recognition attacks \\ \end{tabular} & 95\% \\ \hline
[37] & \
\begin{tabular}{c} **Synthetic** \\ **handwritten** \\ **CAPTCHA** \\ \end{tabular} & Meaningless words &
\begin{tabular}{c} Tesseract, ABBY, \\ and OCR \\ \end{tabular} & **96.42\%** & **94.03\%** \\ \hline
[33] & \begin{tabular}{c} Visual \\ Cryptography \\ \end{tabular} & \
\begin{tabular}{c} Meaningful and \\ meaningless words \\ \end{tabular} &
\begin{tabular}{c} CNN algorithm, \\ GSA software 2.5, \\ and Google Vision \\ \end{tabular} & 92\% & 90\% \\ \hline  
**Institutional Review Board Statement:** Ethical review and approval were waived for this study due to the study involved anonymous data collection with no risk to participants.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are openly available in Zenodo at [[https://doi.org/10.5281/zenodo.14048018](https://doi.org/10.5281/zenodo.14048018)]([https://doi.org/10.5281/zenodo.14048018](https://doi.org/10.5281/zenodo.14048018)), [51].  
**Conflicts of Interest:** The authors declare no conflicts of interest.