Enhancing Security of Online Interfaces: Adversarial Handwritten Arabic CAPTCHA Generation
Ghady Alrasheed, Suliman A. Alsuhibany · Applied Sciences · 2025
With the increasing online activity of Arabic speakers, the development of effective CAPTCHAs (Completely Automated Public Turing Tests to Tell Computers and Humans Apart) tailored for Arabic users has become crucial. Traditional CAPTCHAs, however, are increasingly vulnerable to machine learning-based attacks. To address this challenge, we introduce a method for generating adversarial handwritten Arabic CAPTCHAs that remain user-friendly yet difficult for machines to solve. Our approach involves synthesizing handwritten Arabic words using a simulation technique, followed by the application of five adversarial perturbation techniques: Expectation Over Transformation (EOT), Scaled Gaussian Translation with Channel Shifts (SGTCS), Jacobian-based Saliency Map Attack (JSMA), Immutable Adversarial Noise (IAN), and Connectionist Temporal Classification (CTC). Evaluation results demonstrate that JSMA provides the highest level of security, with 30% of meaningless word CAPTCHAs remaining completely unrecognized by automated systems falling to 6.66% for meaningful words. From a usability perspective, JSMA also achieves the highest accuracy rates, with 75.6% for meaningless words and 90.6% for meaningful words. Our work presents an effective strategy for enhancing the security of Arabic websites and online interfaces against bot attacks, contributing to the advancement of CAPTCHA systems.