Guarding Against Bots with Art: NST-based Deep Learning Approach for CAPTCHA Verification

CAPTCHAs have become less effective in preventing automated bots from accessing internet services, owing to the increasing advancements in computer vision and machine learning. To overcome this challenge, this paper proposes a novel approach using Neural Style Transfer (NST) to generate complex and secure CAPTCHAs using VGG-16. Our approach produces visually challenging CAPTCHAs that are difficult for bots to identify and understand, thereby enhancing the security of internet services. The proposed approach utilizes a grid-like design, with 9 randomly selected images that are stylized using Neural Style Transfer, where the user is prompted to choose pictures that are visually similar to the separate images displayed. This not only makes the CAPTCHA more secure but also more user-friendly. We provide implementation details of our approach, including training and validation, and present experimental data demonstrating its efficacy in thwarting automated attacks. The proposed CAPTCHA system is not susceptible to similarity based attacks. The similarity index results returned the following values, MSE: 12934.04 RMSE:113.72 PSNR: 7.013. Our solution has the potential to provide internet services with higher levels of security against automated bots.