Face recognition CAPTCHA
06 Dec 2012-pp 412-417
TL;DR: 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.
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06 May 2012
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).
Abstract: This paper introduces Avatar CAPTCHA, an image based approach to distinguish human users from computer programs (bots). The proposed CAPTCHA asks users to identify avatar faces from a set of 12 grayscale images comprised of a mix of human and avatar faces. Experimental results indicate that it can be solved 62% of the time by human users with an average success time of 24 seconds and a positive user rating of 90%. It is designed to be secure against computer programs (bots). Using brute force attack the success rate for a bot to solve it is 1/4096.
46 citations
Cites background from "Face recognition CAPTCHA"
...CAPTCHA challenges are also designed using human faces [56], matching distorted faces of several different human subjects [6], finding human face image pairs [57], detection of visually distorted human faces embedded in a complex background [58], distinguishing between human and avatar faces [59] as well as identifying gender of face images [60]....
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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.
Abstract: Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is one of the major security components in the provision of fair web access by differentiating human access from malicious, automated access by bots. Though the CAPTCHA strengthens the security aspect of web access, their accessibility to people with visual impairments has inherent unresolved challenges. This paper presents an accessible CAPTCHA model termed HuMan (human or machine?) which aims at providing an audio-based CAPTCHA for people with visual impairments. The HuMan model incorporates personalization into the CAPTCHA access. The polymorphic nature of resolving the HuMan CAPTCHA facilitates kaleidoscopic behavior in CAPTCHA rendering. The presence of ambient noise and requirement of common sense knowledge to answer the questions presented by HuMan CAPTCHA model makes it friendlier toward human users. The HuMan model has a CAPTCHA preemption feature which enables the user to stop the challenge audio as soon as the answer is identified. The results of experiments conducted on the prototype implementation of HuMan model project the mean success rate of 92.46 % and system usability scale score of 82.44 for persons with visual impairments and 82.63 for sighted users.
11 citations
Cites background from "Face recognition CAPTCHA"
...Apart from the normal images, the recognition of human face is also explored in the image-based approach [21, 22, 33]....
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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.
Abstract: CAPTCHAs are challenge-response tests that aim at preventing unwanted machines, including bots, from accessing web services while providing easy access for humans. Recent advances in artificial-intelligence based attacks show that the level of security provided by many state-of-the-art text-based CAPTCHAs is declining. At the same time, techniques for distorting and obscuring the text, which are used to maintain the level of security, make text-based CAPTCHAs difficult to solve for humans, and thereby further degrade usability. The need for developing alternative types of CAPTCHAs that improve both the current security and the usability levels has been emphasized widely.With this study, we contribute to research through (1) the development of two new face recognition CAPTCHAs (Farett-Gender and Farett-Gender&Age), (2) the security analysis of both procedures, and (3) the provision of empirical evidence that one of the suggested CAPTCHAs (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.
10 citations
01 Jan 2014
TL;DR: AVATar CAPTCHA: Telling computers and humans apart by dividing them by face classification and human behaviour Darryl Felix D’Souza.
Abstract: AVATAR CAPTCHA: TELLING COMPUTERS AND HUMANS APART VIA FACE CLASSIFICATION AND MOUSE DYNAMICS Darryl Felix D’Souza
10 citations
Cites background from "Face recognition CAPTCHA"
...CAPTCHA challenges are also designed using human faces [56], matching distorted faces of several different human subjects [6], finding human face image pairs [57], detection of visually distorted human faces embedded in a complex background [58], distinguishing between human and avatar faces [59] as well as identifying gender of face images [60]....
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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.
Abstract: Today, it is difficult to find an adequate Web site with a registration form that is not protected with some automated human proof test. One of the oldest concepts in Artificial Intelligence as a security mechanism based on the Turing Test is CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart). This test was designed to make a difference between the real users and bots and provide security against malicious attacks. The PROMETHEE-GAIA method was employed for ranking different image-based CAPTCHAs according to their usability in this paper. The aim of this study is 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. The weights of the considered criteria were calculated objectively by the entropy method, and for the subjective weights, Analytical Hierarchy Process (AHP) was used. A defined research model was applied through four phases. The survey included 320 randomly selected Internet users and experts in computer science who were familiar with CAPTCHA tests. The proposed model suggested which CAPTCHA offered better human accuracy and lower machine attack rates compared to the existing approaches. The obtained results were very helpful to the web administrators because they indicated that this approach could provide useful insights for the decision-makers about the appropriate mechanisms to protect users against cyber-criminal activities and Internet threats.
9 citations
References
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Book•
01 Jan 2014
TL;DR: Digital image processing 3rd edition free ebooks download, ece 643 digital image processing i chapter 5, gonzfm i xxii 5 1.
Abstract: amazon com digital image processing 3rd edition, digital image processing 3rd edition pdf, digital image processing 3rd edition 9780131687288, een iust ac ir, download digital image processing 3rd edition pdf ebook, digital image processing gonzalez ebay, digital image processing 3rd edition, digital image processing 3rd edition free ebooks download, ece 643 digital image processing i chapter 5, gonzfm i xxii 5 1
1,830 citations
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.
Abstract: CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are widespread security measures on the World Wide Web that prevent automated programs from abusing online services. They do so by asking humans to perform a task that computers cannot yet perform, such as deciphering distorted characters. Our research explored whether such 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. We showed that this method can transcribe text with a word accuracy exceeding 99%, matching the guarantee of professional human transcribers. Our apparatus is deployed in more than 40,000 Web sites and has transcribed over 440 million words.
1,155 citations
"Face recognition CAPTCHA" refers methods in this paper
...The proposed CAPTCHA generation can be represented as, 𝐶 = 𝐹 (f , 𝜙)....
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01 Nov 2006
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.
Abstract: A key goal of computer vision researchers is to create automated face recognition systems that can equal, and eventually surpass, human performance. To this end, it is imperative that computational researchers know of the key findings from experimental studies of face recognition by humans. These findings 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. In this paper, we present what we believe are 19 basic results, with implications for the design of computational systems. Each result is described briefly and appropriate pointers are provided to permit an in-depth study of any particular result
699 citations
"Face recognition CAPTCHA" refers background in this paper
...It is well established that humans are good at recognizing both familiar and unfamiliar faces [11], [14]....
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Proceedings Article•
01 Oct 2007TL;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.
Abstract: We present Asirra (Figure 1), a CAPTCHA that asks users to identify cats out of a set of 12 photographs of both cats and dogs. Asirra is easy for users; user studies indicate it can be solved by humans 99.6% of the time in under 30 seconds. Barring a major advance in machine vision, we expect computers will have no better than a 1/54,000 chance of solving it. Asirra’s image database is provided by a novel, mutually beneficial partnership with Petfinder.com. In exchange for the use of their three million images, we display an “adopt me” link beneath each one, promoting Petfinder’s primary mission of finding homes for homeless animals. We describe the design of Asirra, discuss threats to its security, and report early deployment experiences. We also describe two novel algorithms for amplifying the skill gap between humans and computers that can be used on many existing CAPTCHAs.
519 citations
"Face recognition CAPTCHA" refers background in this paper
...Estimating CAPTCHA Parameters: Since CAPTCHA generation is dependent on several parameters, the training stage involves learning the useful sets of parameters....
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PARC1
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.
Abstract: The Asirra CAPTCHA [EDHS2007], proposed at ACM CCS 2007, relies on the problem of distinguishing images of cats and dogs (a task that humans are very good at). The security of Asirra is based on the presumed difficulty of classifying these images automatically.In this paper, we describe a classifier which is 82.7% accurate in telling apart the images of cats and dogs used in Asirra. This classifier is a combination of support-vector machine classifiers trained on color and texture features extracted from images. Our classifier allows us to solve a 12-image Asirra challenge automatically with probability 10.3%. This probability of success is significantly higher than the estimate of 0.2% given in [EDHS2007] for machine vision attacks. Our results suggest caution against deploying Asirra without safeguards.We also investigate the impact of our attacks on the partial credit and token bucket algorithms proposed in [EDHS2007]. The partial credit algorithm weakens Asirra considerably and we recommend against its use. The token bucket algorithm helps mitigate the impact of our attacks and allows Asirra to be deployed in a way that maintains an appealing balance between usability and security. One contribution of our work is to inform the choice of safeguard parameters in Asirra deployments.
193 citations