Research trends on CAPTCHA: A systematic literature

Abstract

The advent of technology has crept into virtually all sectors and this has culminated in automated processes making use of the Internet in executing various tasks and actions. Web services have now become the trend when it comes to providing solutions to mundane tasks. However, this development comes with the bottleneck of authenticity and intent of users. Providers of these Web services, whether as a platform, as a software or as an Infrastructure use various human interaction proof’s (HIPs) to validate authenticity and intent of its users. Completely automated public turing test to tell computer and human apart (CAPTCHA), a form of IDS in web services is advantageous. Research into CAPTCHA can be grouped into two -CAPTCHA development and CAPTCH recognition. Selective learning and convolutionary neural networks (CNN) as well as deep convolutionary neural network (DCNN) have become emerging trends in both the development and recognition of CAPTCHAs. This paper reviews critically over fifty article publications that shows the current trends in the area of the CAPTCHA scheme, its development and recognition mechanisms and the way forward in helping to ensure a robust and yet secure CAPTCHA development in guiding future research endeavor in the subject domain.

A systematic literature review of machine learning methods in predicting court decisions

“…The chosen studies were assessed based on the QA questions explained in Section 2.4, and the analysis is presented in Table 4. The table demonstrates that 17 studies received high scores between the total score of three-point-five (3.5) to five (5), whereas five studies obtained a moderate score of 3. Conversely, four studies that obtained low scores were excluded from the review.…”

Adaptive-CAPTCHA: A Text CAPTCHA Solver based on CRNN and Configurable Filter Networks

“…In recent years, the field of text-based CAPTCHA recognition has seen several advancements, particularly in the realm of deep learning, which has become the mainstream direction for text-based CAPTCHA recognition [11]. CNNs are the most used networks as they excel at learning spatial hierarchies in an image, thereby providing a robust way for CAPTCHA recognition [12]. Recurrent Neural Network (RNN) or Long Short-Term Memory (LSTM) is another neural network, which has shown impressive recognition accuracy in CAPTCHAs with CCT as it can remember patterns over lengthy sequences.…”