# Enhancing Security of Online Interfaces: Adversarial Handwritten Arabic CAPTCHAs Generation
## 5 Discussion
### Comparison to Previous Studies  
The usability evaluation helps to understand the user experience when solving different CAPTCHAs. The security assessment examines the effectiveness of various perturbation techniques against modern bots.  
Table 13 summarizes results from five previous studies evaluating Arabic CAPTCHAs techniques. These can be compared with the results of our study.  
There have been several new techniques put in place to generate Arabic handwritten CAPTCHAs:  
* Synthetic Arabic handwritten CAPTCHA methods that use machine learning to generate authentic looking handwritten words [37].
* Distorting Arabic word images through OCR procedures to improve the security of Arabic CAPTCHAs [35].
* A process of handwritten text segmentation that includes creating synthetic cursive Arabic word images and asking users to verify CAPTCHAs by recognizing segmentation points [34].
* Visual cryptography that splits images into two components that need to be properly aligned for decryption [33].  
All of these methods were important first attempts in the development and assessment of Arabic handwritten CAPTCHAs.  
In terms of security, each study introduced innovative approaches to generating and evaluating Arabic handwritten CAPTCHAs. All methods demonstrated acceptable security results [33; 34; 35; 37].  
Analyzing usability, the research found high accuracy and success rates, indicating that the methods were easy for people to understand [33; 34; 35; 37]. The focus on usability is crucial for acceptance.  
Lines and flips [37] and adversarial distortions both try to fool text recognition. Lines and flips use visual changes like flipping text or adding wavy lines, while adversarial distortions create a lack of clarity in the image by leveraging gradient information from the target ML model. Adversarial examples are algorithmically designed to maximize neural network confusion. Tests showed adversarial distortion techniques like JSMA worked better at disrupting machines' ability to read text, while lines and flips are more visible and predictable, which makes it easier for machines to recognize the text despite visual changes. The results indicated that the machines' ability to recognize text reached 96.42%. However, this form of OCR is less effective than Google Vision, which leverages predictability, machine learning algorithms trained on structured text, and an ability to counteract adversarial distortions by maintaining high recognition accuracy through leveraging its training on predictable text structures. However, with techniques like JSMA, our adversarial perturbations studies still achieved a recognition rate of 86.66%, demonstrating the ongoing potential of these approaches.  
In terms of usability, both methods are highly accurate, with lines and flips in the study [37] having a usability of 90-95% and JSMA in our study having a usability of 90.6%.  
Our study proposes a new dimension by introducing adversarial perturbations. This approach provides deeper insights into the security of Arabic handwritten CAPTCHAs. Our research utilizes an adversarial perturbation method to generate adversarial Arabic written CAPTCHAs and to evaluate the security and usability of various adversarial perturbation techniques. Five methods, CTC, EOT, IAN, JSMA, and SGTCS, were tested.