# Enhancing Security of Online Interfaces: Adversarial Handwritten Arabic CAPTCHAs Generation
## 2 Literature Review
### Arabic Handwritten CAPTCHAs  
Arabic handwritten CAPTCHA has received considerable attention in the CAPTCHA scheme literature over the past five years [31]. For example, [PERSON] et al. [32] introduced a novel approach using handwritten Arabic calligraphy to enhance security, demonstrating that stylized scripts significantly reduce automated recognition while maintaining human readability.  
Recent research aims to create and evaluate Arabic handwritten CAPTCHAs to improve security and usability. A visual cryptography method was developed by [PERSON] and [PERSON] [33] for Arabic CAPTCHAs that are handwritten and printed. They encrypted images into two parts that needed to be stacked correctly for decryption. The evaluation showed that the approach provided acceptable usability and security results for Arabic users.  
A novel Arabic CAPTCHA scheme based on handwritten text segmentation was proposed by [PERSON] and [PERSON] [34]. They used a technique that included generating synthetic images of cursive Arabic words and asking users to validate the CAPTCHAs by finding the segmentation points between letters. According to experimental results, this method maintained security and usability while providing challenges to users.  
The technique suggested by [PERSON] and [PERSON] [35] includes the distortion of pre-written Arabic word images with OCR processes to generate secure Arabic handwritten CAPTCHAs. With an accuracy rate of 88-90% in usability and a less than 0.5% risk in security, this method demonstrates a successful balance between usability and security.  
[PERSON] et al. [36] developed a robust system capable of producing realistic Arabic handwriting datasets from ASCII text--a crucial resource for various applications including CAPTCHA development--by carefully selecting a diverse set of simple Arabic words and refining the Gaussian Mixture Model process. This was an improvement in the datasets that were available for training and evaluating Arabic CAPTCHA models.  
[PERSON] et al. [37] proposed a synthetic Arabic handwritten CAPTCHA scheme. They developed generative models using machine learning techniques to synthesize authentic-looking handwritten words. Their evaluation showed the capacity to generate authentic word pictures, achieving a human accuracy rate of over 80% in word identification.  
Generative Adversarial Networks (GANs), as explored in [PERSON] et al, [38] are an approach for generating handwritten Arabic characters. GAN models can generate more challenging and reliable CAPTCHAs by creating high-quality, diverse character samples. This reduces the risk of automated attacks, while also improving the security of online systems.  
Improvements in Arabic handwritten CAPTCHAs are useful in a variety of fields, including education. For example, [PERSON] et al. [39] developed a gamified Arabic CAPTCHA system that improves security and promotes language acquisition. It combines CAPTCHA features with game elements such as word completion, ordering, and sentence creation in Arabic. This approach involves engaging users while preventing bot attacks. The system's ability to improve users' Arabic vocabulary and grammar demonstrates its educational potential.  
[PERSON] et al. [40] pioneered the use of Arabic CAPTCHAs for cybersecurity, highlighting challenges such as the script's complexity, cultural relevance, and accessibility barriers. Their work emphasized the necessity of adapting CAPTCHA designs to Arabic's cursive nature and contextual letterforms while addressing usability concerns for users with disabilities. Building on this foundation, recent advancements in machine learning and gamification offer opportunities to enhance both security and user-centric design, underscoring the need for tailored solutions that balance linguistic specificity with robust protection against automated attacks