A multibiometrics-based CAPTCHA for improved online security
01 Sep 2016-pp 1-8
TL;DR: This paper proposes a new CAPTCHA incorporating multiple biometric modalities that achieves high human accuracy while being resistant to existing attacks on CAPTCHAs and to detection by state-of-the-art software.
Abstract: CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart) have been a common tool for preventing unauthorized access to websites for over a decade, but increasingly sophisticated optical character recognition algorithms and attack strategies have rendered traditional CAPTCHAs insecure. In this paper, we propose a new CAPTCHA incorporating multiple biometric modalities. Users are asked to identify faces, eyes, and fingerprints in a complex composite image. With over 1,900 volunteers and 30,000+ attempts, the proposed approach achieves high human accuracy while being resistant to existing attacks on CAPTCHAs and to detection by state-of-the-art software.
Citations
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01 Jan 2006
TL;DR: It is concluded that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work, and the efficacy of this algorithm is evaluated against the variables of gender and racial origin.
Abstract: This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus for face recognition is highlighted in the evaluation of a standard face recognition algorithm, which illustrates the impact that age-progression, has on recognition rates. Assessment of the efficacy of this algorithm is evaluated against the variables of gender and racial origin. This work further concludes that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work.
139 citations
29 May 2018
TL;DR: This paper presents a neural network that leverages Mozilla's open source implementation of Baidu's Deep Speech architecture and is currently able to solve the audio version of an open-source CATPCHA system (named SimpleCaptcha), with 98.8% accuracy.
Abstract: A Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a defensive mechanism designed to differentiate humans and computers to prevent unauthorized use of online services by automated attacks. They often consist of a visual or audio test that humans can perform easily but that bots cannot solve. However, with current machine learning techniques and open-source neural network architectures, it is now possible to create a self-contained system that is able to solve specific CAPTCHA types and outperform some human users. In this paper, we present a neural network that leverages Mozilla's open source implementation of Baidu's Deep Speech architecture; our model is currently able to solve the audio version of an open-source CATPCHA system (named SimpleCaptcha) with 98.8% accuracy. Our network was trained on 100,000 audio samples generated from SimpleCaptcha and can solve new SimpleCaptcha audio tests in 1.25 seconds on average (with a standard deviation of 0.065 seconds). Our implementation seems additionally promising because it does not require a powerful server to function and is robust to adversarial examples that target Deep Speech's pre-trained models.
7 citations
Cites background from "A multibiometrics-based CAPTCHA for..."
...There already exist many proposals for replacing visual CAPTCHAs, most of which require the user to identify items ranging from biometric features like eyes [7] to everyday objects like chairs [6] in complex scenes....
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01 Dec 2017
TL;DR: A new CAPTCHA based on eye movement characteristics, namely, EYE-CAPTCHA is proposed, which employs gaze detection and eye movement control and can achieve more reliability for differentiating human and machine.
Abstract: CAPTCHA is a system used to distinguish between human and machine, typically as a method to prevent spam and automatic data extraction from a bot or an attacker. As a result of continuing advances in computer capability and artificial intelligence, distinguishing between human and computer becomes more difficult. With this reason, some spammers can break CAPTCHA and attack the system with an appropriate algorithm. To improve more reliability, biometric authentication can be used to enhance the system security by verifying the identity of a person based on physiological or behavioral characteristics. One of the promising methods for biometric authentication is the identification through unique eye movement characteristics. This paper proposes a new CAPTCHA based on eye movement characteristics, namely, EYE-CAPTCHA. The proposed method employs gaze detection and eye movement control. With this proposed system, the system can achieve more reliability for differentiating human and machine.
6 citations
Cites background from "A multibiometrics-based CAPTCHA for..."
..., propose multibiometrics-based CAPTCHA for improved online security [13]....
[...]
25 May 2017
TL;DR: Inspired by the negative selection approach in biological immune systems, an innovative two-phase filtering algorithm is proposed which ensures that the CAPTCHA is resilient to automated attack while remaining easy for human users to solve.
Abstract: The growth of online services has resulted in a great need for tools to secure systems from would-be attackers without compromising the user experience. CAPTCHAs (Completely Automated Public Turing Tests to Tell Computers and Humans Apart) are one tool for this purpose, but their popular text-based form has been rendered insecure by improvements in character recognition technology. In this paper, we propose a novel imagebased CAPTCHA which employs object recognition as its test. Inspired by the negative selection approach in biological immune systems, an innovative two-phase filtering algorithm is proposed which ensures that the CAPTCHA is resilient to automated attack while remaining easy for human users to solve. In extensive testing involving over 3,000 participants, the proposed aiCAPTCHA achieved a 92.0% human success rate.
5 citations
Cites background from "A multibiometrics-based CAPTCHA for..."
...[5] or biometric features [6], and conducting limited class object recognition such as distinguishing between dogs and cats [7]....
[...]
TL;DR: In this paper, a human verification scheme was presented to overcome the vulnerabilities of attacks and to enhance security, where a hand image-based CAPTCHA (HandCAPTCHA) was tested to avert automated bot-attacks on the subsequent biometric stage.
Abstract: This paper presents a human verification scheme in two independent stages to overcome the vulnerabilities of attacks and to enhance security. At the first stage, a hand image-based CAPTCHA (HandCAPTCHA) is tested to avert automated bot-attacks on the subsequent biometric stage. In the next stage, finger biometric verification of a legitimate user is performed with presentation attack detection (PAD) using the real hand images of the person who has passed a random HandCAPTCHA challenge. The electronic screen-based PAD is tested using image quality metrics. After this spoofing detection, geometric features are extracted from the four fingers (excluding the thumb) of real users. A modified forward–backward (M-FoBa) algorithm is devised to select relevant features for biometric authentication. The experiments are performed on the Bogazici University (BU) and the IIT-Delhi (IITD) hand databases using the k-nearest neighbor and random forest classifiers. The average accuracy of the correct HandCAPTCHA solution is 98.5%, and the false accept rate of a bot is 1.23%. The PAD is tested on 255 subjects of BU, and the best average error is 0%. The finger biometric identification accuracy of 98% and an equal error rate (EER) of 6.5% have been achieved for 500 subjects of the BU. For 200 subjects of the IITD, 99.5% identification accuracy, and 5.18% EER are obtained.
4 citations
References
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TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Abstract: This paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a simple and efficient classifier which is built using the AdaBoost learning algorithm (Freund and Schapire, 1995) to select a small number of critical visual features from a very large set of potential features. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. A set of experiments in the domain of face detection is presented. The system yields face detection performance comparable to the best previous systems (Sung and Poggio, 1998; Rowley et al., 1998; Schneiderman and Kanade, 2000; Roth et al., 2000). Implemented on a conventional desktop, face detection proceeds at 15 frames per second.
13,037 citations
07 Jul 2001
TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.
Abstract: This paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the "Integral Image" which allows the features used by our detector to be computed very quickly. The second is a simple and efficient classifier which is built using the AdaBoost learning algo- rithm (Freund and Schapire, 1995) to select a small number of critical visual features from a very large set of potential features. The third contribution is a method for combining classifiers in a "cascade" which allows back- ground regions of the image to be quickly discarded while spending more computation on promising face-like regions. A set of experiments in the domain of face detection is presented. The system yields face detection perfor- mance comparable to the best previous systems (Sung and Poggio, 1998; Rowley et al., 1998; Schneiderman and Kanade, 2000; Roth et al., 2000). Implemented on a conventional desktop, face detection proceeds at 15 frames per second.
10,592 citations
Book•
10 Mar 2005TL;DR: This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators.
Abstract: A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators
3,821 citations
"A multibiometrics-based CAPTCHA for..." refers methods in this paper
...Fingerprint images are from the FVC2004 database [12]....
[...]
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
10 Apr 2006
TL;DR: The MORPH dataset as discussed by the authors is a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc.
Abstract: This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus for face recognition is highlighted in the evaluation of a standard face recognition algorithm, which illustrates the impact that age-progression, has on recognition rates. Assessment of the efficacy of this algorithm is evaluated against the variables of gender and racial origin. This work further concludes that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work.
1,051 citations