Augment CAPTCHA Security Using Adversarial Examples with Neural Style Transfer

To counteract rising bots, many CAPTCHAs (Completely Automated Public Turing tests to tell Computers and Humans Apart) have been developed throughout the years. attacks [1], however, employing powerful deep learning techniques, had high success rates over common CAPTCHAs, including image-based text-based CAPTCHAs. Optimistically, introducing imperceptible noise, Adversarial Examples lately shown particularly impact DNN (Deep Neural Network) networks. The authors improved CAPTCHA security architecture by increasing resilience of when combined with Style Transfer. findings demonstrated that proposed approach considerably improves ordinary