Yan, XLiu, FYan, Wei QiLu, Y2020-03-042020-03-042020-03-042020-03-04Mathematics. 2020; 8(3):332.2227-7390https://hdl.handle.net/10292/13187Nowadays, lots of applications and websites utilize text-based captchas to partially protect the authentication mechanism. However, in recent years, different ways have been exploited to automatically recognize text-based captchas especially deep learning-based ways, such as, convolutional neural network (CNN). Thus, we have to enhance the text captchas design. In this paper, using the features of the randomness for each encoding process in visual cryptography (VC) and the visual recognizability with naked human eyes, VC is applied to design and enhance text-based captcha. Experimental results using two typical deep learning-based attack models indicate the effectiveness of the designed method. By using our designed VC-enhanced text-based captcha (VCETC), the recognition rate is in some degree decreased.© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Text captchaVisual cryptographyRandom gridsVisual cryptography applicationEnhanced text captchaJournal ArticleOpenAccess10.3390/math8030332