27 research outputs found
A security analysis of automated Chinese turing tests
Text-based Captchas have been widely used to deter misuse of services on the Internet. However, many designs have been broken. It is intellectually interesting and practically relevant to look for alternative designs, which are currently a topic of active research. We motivate the study of Chinese Captchas as an interesting alternative design - counterintuitively, it is possible to design Chinese Captchas that are universally usable, even to those who have never studied Chinese language. More importantly, we ask a fundamental question: is the segmentation-resistance principle established for Roman-character based Captchas applicable to Chinese based designs? With deep learning techniques, we offer the first evidence that computers do recognize individual Chinese characters well, regardless of distortion levels. This suggests that many real-world Chinese schemes are insecure, in contrast to common beliefs. Our result offers an essential guideline to the design of secure Chinese Captchas, and it is also applicable to Captchas using other large-alphabet languages such as Japanese
CAPTCHA Types and Breaking Techniques: Design Issues, Challenges, and Future Research Directions
The proliferation of the Internet and mobile devices has resulted in
malicious bots access to genuine resources and data. Bots may instigate
phishing, unauthorized access, denial-of-service, and spoofing attacks to
mention a few. Authentication and testing mechanisms to verify the end-users
and prohibit malicious programs from infiltrating the services and data are
strong defense systems against malicious bots. Completely Automated Public
Turing test to tell Computers and Humans Apart (CAPTCHA) is an authentication
process to confirm that the user is a human hence, access is granted. This
paper provides an in-depth survey on CAPTCHAs and focuses on two main things:
(1) a detailed discussion on various CAPTCHA types along with their advantages,
disadvantages, and design recommendations, and (2) an in-depth analysis of
different CAPTCHA breaking techniques. The survey is based on over two hundred
studies on the subject matter conducted since 2003 to date. The analysis
reinforces the need to design more attack-resistant CAPTCHAs while keeping
their usability intact. The paper also highlights the design challenges and
open issues related to CAPTCHAs. Furthermore, it also provides useful
recommendations for breaking CAPTCHAs
Diff-CAPTCHA: An Image-based CAPTCHA with Security Enhanced by Denoising Diffusion Model
To enhance the security of text CAPTCHAs, various methods have been employed,
such as adding the interference lines on the text, randomly distorting the
characters, and overlapping multiple characters. These methods partly increase
the difficulty of automated segmentation and recognition attacks. However,
facing the rapid development of the end-to-end breaking algorithms, their
security has been greatly weakened. The diffusion model is a novel image
generation model that can generate the text images with deep fusion of
characters and background images. In this paper, an image-click CAPTCHA scheme
called Diff-CAPTCHA is proposed based on denoising diffusion models. The
background image and characters of the CAPTCHA are treated as a whole to guide
the generation process of a diffusion model, thus weakening the character
features available for machine learning, enhancing the diversity of character
features in the CAPTCHA, and increasing the difficulty of breaking algorithms.
To evaluate the security of Diff-CAPTCHA, this paper develops several attack
methods, including end-to-end attacks based on Faster R-CNN and two-stage
attacks, and Diff-CAPTCHA is compared with three baseline schemes, including
commercial CAPTCHA scheme and security-enhanced CAPTCHA scheme based on style
transfer. The experimental results show that diffusion models can effectively
enhance CAPTCHA security while maintaining good usability in human testing
Handwritten Digit Recognition and Classification Using Machine Learning
In this paper, multiple learning techniques based on Optical character recognition (OCR) for the handwritten digit recognition are examined, and a new accuracy level for recognition of the MNIST dataset is reported. The proposed framework involves three primary parts, image pre-processing, feature extraction and classification. This study strives to improve the recognition accuracy by more than 99% in handwritten digit recognition. As will be seen, pre-processing and feature extraction play crucial roles in this experiment to reach the highest accuracy
A Review on Human-Computer Interaction and Intelligent Robots
In the field of artificial intelligence, human–computer interaction (HCI) technology and its related intelligent robot technologies are essential and interesting contents of research. From the perspective of software algorithm and hardware system, these above-mentioned technologies study and try to build a natural HCI environment. The purpose of this research is to provide an overview of HCI and intelligent robots. This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction. Based on these same technologies, this research introduces some intelligent robot systems and platforms. This paper also forecasts some vital challenges of researching HCI and intelligent robots. The authors hope that this work will help researchers in the field to acquire the necessary information and technologies to further conduct more advanced research