reCAPTCHA: Human-Based Character Recognition via Web Security Measures

Ahn, Luis von; Maurer, Benjamin; McMillen, Colin; Abraham, David; Blum, Manuel

doi:10.1126/science.1160379

Cited by 844 publications

(505 citation statements)

References 4 publications

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“…1 shows examples of different text-based CAPTCHAs used by Microsoft's Hotmail. Since CAPTCHA was first introduced by Von Ahn [3] in 2000, hundreds of different text-based variations have been introduced. Yahoo!…”

Section: Text-based Systemsmentioning

confidence: 99%

Random Image Matching CAPTCHA System

Hajjdiab

Ghazal²,

Khalil³

2017

ELCVIA

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Online security risks is an important issue and caught the attention of researchers in the area of networks, web development, human computer interaction and software engineering. One main challenge for online systems is to identify whether the users are humans or software robots (bots). While it is natural to provide service to human users, providing service for software robots (bots) comes with many security risks and challenges. Software robots are often used by spammers to create fake online accounts, affect search engine ranking, take part in on-line polls, send out spam or simply waste the resources of the server. In this paper we introduce a visual CAPTCHA technique that is based on generating random images by the computer, the user is then asked to match a feature point between two images (i.e. solve the correspondence problem as defined by the researchers in the computer vision area). The relationship between the two images is based on a randomly generated homography transformation function. The main advantage of our approach compared to other visual CAPTCHA techniques is that we eliminate the need for a database of images while retaining ease of use.

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Section: Text-based Systemsmentioning

confidence: 99%

Random Image Matching CAPTCHA System

Hajjdiab

Ghazal²,

Khalil³

2017

ELCVIA

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“…In particular, harvesting the labels y i usually involves human interaction. How to do this in a costeffective way by, for example, using CAPTCHAs to obtain character labels [36] or making people label images by playing a game [37,38], is itself a challenging research subject. On the other hand, in experimental physics simulators can be used to sample from the distribution p(x | y), so in these applications data collection is usually not an issue.…”

Section: Applicationsmentioning

confidence: 99%

Introduction to multivariate discrimination

Kégl

2013

EPJ Web of Conferences

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Abstract.Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either relevant to or even motivated by certain unorthodox applications of multivariate discrimination in experimental physics.

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“…Prevention-based approaches employ series of mechanisms to keep spammers out of social tagging systems, such as CAPTCHA [16] and reCAPTCHA [17], or make it hard for spammers to pollute social system by restricting access, limiting number of resources a user can interact with, or requiring registration fee. Usually, prevention-based approaches are used as complementary defense systems to rank-or identification-based approaches.…”

Section: Spam Fighting In Social Tagging Systemsmentioning

confidence: 99%

Spam Fighting in Social Tagging Systems

Yazdani

Ivanov

Analoui

et al. 2012

Lecture Notes in Computer Science

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Abstract. Tagging in online social networks is very popular these days, as it facilitates search and retrieval of diverse resources available online. However, noisy and spam annotations often make it difficult to perform an efficient search. Users may make mistakes in tagging and irrelevant tags and resources may be maliciously added for advertisement or self-promotion. Since filtering spam annotations and spammers is time-consuming if it is done manually, machine learning approaches can be employed to facilitate this process. In this paper, we propose and analyze a set of distinct features based on user behavior in tagging and tags popularity to distinguish between legitimate users and spammers. The effectiveness of the proposed features is demonstrated through a set of experiments on a dataset of social bookmarks.

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reCAPTCHA: Human-Based Character Recognition via Web Security Measures

Cited by 844 publications

References 4 publications

Random Image Matching CAPTCHA System

Random Image Matching CAPTCHA System

Introduction to multivariate discrimination

Spam Fighting in Social Tagging Systems

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