Proceedings ArticleDOI
Face recognition CAPTCHA
Gaurav Goswami,Richa Singh,Mayank Vatsa,Brian M. Powell,Afzel Noore +4 more
- pp 412-417
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TLDR
The proposed algorithm generates CAPTCHA that offer better human accuracy and lower attack rates compared to existing approaches.Abstract:
CAPTCHA is one of the Turing tests used to classify human users and automated scripts. Existing CAPTCHAs, especially text-based CAPTCHAs, are used in many applications, however they pose challenges due to language dependency and high attack rates. In this paper, we propose a face recognition-based CAPTCHA as a potential solution. To solve the CAPTCHA, users must correctly find one pair of human face images, that belong to same subject, embedded in a complex background without selecting any nonface image or impostor pair. The proposed algorithm generates CAPTCHA that offer better human accuracy and lower attack rates compared to existing approaches.read more
Citations
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Proceedings ArticleDOI
Avatar CAPTCHA: Telling computers and humans apart via face classification
TL;DR: The proposed Avatar CAPTCHA asks users to identify avatar faces from a set of 12 grayscale images comprised of a mix of human and avatar faces to be secure against computer programs (bots).
Journal ArticleDOI
HuMan: an accessible, polymorphic and personalized CAPTCHA interface with preemption feature tailored for persons with visual impairments
TL;DR: The HuMan model has a CAPTCHA preemption feature which enables the user to stop the challenge audio as soon as the answer is identified, and the polymorphic nature of resolving the HuManCAPTCHA facilitates kaleidoscopic behavior in CAPTcha rendering.
Journal ArticleDOI
Development of two novel face-recognition CAPTCHAs
TL;DR: This study contributes to research through the development of two new face recognition CAPTCHAs, the provision of empirical evidence that one of the suggested CAPT CHAs (Farett-Gender) is similar to Google's reCAPTCHA and better than KCAPTCHA concerning effectiveness (error rates), superior to both regarding learnability and satisfaction but not efficiency.
DissertationDOI
Avatar captcha : telling computers and humans apart via face classification and mouse dynamics.
TL;DR: AVATar CAPTCHA: Telling computers and humans apart by dividing them by face classification and human behaviour Darryl Felix D’Souza.
Journal ArticleDOI
An investigation of the usability of image-based CAPTCHAs using PROMETHEE-GAIA method
TL;DR: A comparative analysis of seven image-based CAPTCHAs based on three different criteria: time to find a solution, a number of attempts, and task difficulty suggested which CAPTCHA offered better human accuracy and lower machine attack rates compared to the existing approaches.
References
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Book
Digital Image Processing 3rd Edition
TL;DR: Digital image processing 3rd edition free ebooks download, ece 643 digital image processing i chapter 5, gonzfm i xxii 5 1.
Journal ArticleDOI
reCAPTCHA: Human-Based Character Recognition via Web Security Measures
TL;DR: This research explored whether human effort can be channeled into a useful purpose: helping to digitize old printed material by asking users to decipher scanned words from books that computerized optical character recognition failed to recognize.
Journal ArticleDOI
Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About
TL;DR: Findings from experimental studies of face recognition by humans provide insights into the nature of cues that the human visual system relies upon for achieving its impressive performance and serve as the building blocks for efforts to artificially emulate these abilities.
Proceedings Article
Asirra: a CAPTCHA that exploits interest-aligned manual image categorization.
TL;DR: A CAPTCHA that asks users to identify cats out of a set of 12 photographs of both cats and dogs, and two novel algorithms for amplifying the skill gap between humans and computers that can be used on many existing CAPTCHAs are described.
Proceedings ArticleDOI
Machine learning attacks against the Asirra CAPTCHA
TL;DR: In this article, a classifier is proposed which is 82.7% accurate in telling apart the images of cats and dogs used in Asirra, which is significantly higher than the estimate of 0.2% given in [EDHS2007] for machine vision attacks.