Cursor CAPTCHA &amp;#x2014; Implementing CAPTCHA using mouse cursor

Thomas, Varun Ambrose; Kaur, Karanvir

doi:10.1109/wocn.2013.6616188

Cited by 7 publications

(7 citation statements)

References 11 publications

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“…The user needs to drag this image until he can recognize a well formed text. Cursor CAPTCHA [38] changes the appearance of mouse cursor into another random object. The user needs to overlap the cursor on the identical object placed in a random generated image.…”

Section: B Image-based Captchasmentioning

confidence: 99%

“…On the other hand, our proposal is designed to resist all of them, while maintaining a high usability level. [27] low high 0.20% Motion [36] low high 25.00% Video [20] low high 0.30% Noise [29] mid mid ∼ 0.00% Cursor [38] low low ∼ 0.00% Jigsaw [14] low mid 6.66% PlayThru [1] high high ∼ 0.00% CAPTCHaStar high high 0.09%…”

Section: A Comparison With Other Image-based Captchasmentioning

confidence: 99%

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CAPTCHaStar! A Novel CAPTCHA Based on Interactive Shape Discovery

Conti

Guarisco

Spolaor

2016

Lecture Notes in Computer Science

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Abstract-Over the last years, most websites on which users can register (e.g., email providers and social networks) adopted CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) as a countermeasure against automated attacks. The battle of wits between designers and attackers of CAPTCHAs led to current ones being annoying and hard to solve for users, while still being vulnerable to automated attacks.In this paper, we propose CAPTCHaStar, a new image-based CAPTCHA that relies on user interaction. This novel CAPTCHA leverages the innate human ability to recognize shapes in a confused environment. We assess the effectiveness of our proposal for the two key aspects for CAPTCHAs, i.e., usability, and resiliency to automated attacks. In particular, we evaluated the usability, carrying out a thorough user study, and we tested the resiliency of our proposal against several types of automated attacks: traditional ones; designed ad-hoc for our proposal; and based on machine learning. Compared to the state of the art, our proposal is more user friendly (e.g., only some 35% of the users prefer current solutions, such as text-based CAPTCHAs) and more resilient to automated attacks.

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Section: B Image-based Captchasmentioning

confidence: 99%

Section: A Comparison With Other Image-based Captchasmentioning

confidence: 99%

CAPTCHaStar! A Novel CAPTCHA Based on Interactive Shape Discovery

Conti

Guarisco

Spolaor

2016

Lecture Notes in Computer Science

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“…Cursor CAPTCHA [34] Overlap the cursor on the target object in a randomly generated image Background, noise, random location of target Asirra [35] Choose a cat from a collection of 12 images of cats and dogs Grids, categorization of cats and dogs, location API integration (hence, poisoned database attacks)…”

Section: Interactive-basedmentioning

confidence: 99%

“…Users must drag the small noisy image to identify the hidden object in the large image before clicking the "submit" button to pass the CAPTCHA challenge. Cursor CAPTCHA, proposed by Thomas et al [34], displays five cursors randomly in a generated image. To pass the challenge, users must overlap the mouse pointer onto a specific cursor.…”

Section: Grids Object Recognition User Activity Trackingmentioning

confidence: 99%

Human-artificial intelligence approaches for secure analysis in CAPTCHA codes

Dinh

Ogiela

2022

EURASIP J. on Info. Security

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CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) has long been used to keep automated bots from misusing web services by leveraging human-artificial intelligence (HAI) interactions to distinguish whether the user is a human or a computer program. Various CAPTCHA schemes have been proposed over the years, principally to increase usability and security against emerging bots and hackers performing malicious operations. However, automated attacks have effectively cracked all common conventional schemes, and the majority of present CAPTCHA methods are also vulnerable to human-assisted relay attacks. Invisible reCAPTCHA and some approaches have not yet been cracked. However, with the introduction of fourth-generation bots accurately mimicking human behavior, a secure CAPTCHA would be hardly designed without additional special devices. Almost all cognitive-based CAPTCHAs with sensor support have not yet been compromised by automated attacks. However, they are still compromised to human-assisted relay attacks due to having a limited number of challenges and can be only solved using trusted devices. Obviously, cognitive-based CAPTCHA schemes have an advantage over other schemes in the race against security attacks. In this study, as a strong starting point for creating future secure and usable CAPTCHA schemes, we have offered an overview analysis of HAI between computer users and computers under the security aspects of open problems, difficulties, and opportunities of current CAPTCHA schemes.

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“…Thomas et al [120] propose Cursor CAPTCHA, which displays five cursor images in a randomly generated image and customizes the cursor image of the mouse pointer. Then, the CAPTCHA asks users to overlap the mouse pointer on an identical cursor image to pass the challenge.…”

Section: 26mentioning

confidence: 99%

Gotta CAPTCHA 'Em All: A Survey of Twenty years of the Human-or-Computer Dilemma

Guerar,

Verderame,

Migliardi

et al. 2021

Preprint

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A recent study has found that malicious bots generated nearly a quarter of overall website traffic in 2019 [100]. These malicious bots perform activities such as price and content scraping, account creation and takeover, credit card fraud, denial of service, etc. Thus, they represent a serious threat to all businesses in general, but are especially troublesome for e-commerce, travel and financial services. One of the most common defense mechanisms against bots abusing online services is the introduction of Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), so it is extremely important to understand which CAPTCHA schemes have been designed and their actual effectiveness against the ever-evolving bots. To this end, this work provides an overview of the current state-of-the-art in the field of CAPTCHA schemes and defines a new classification that includes all the emerging schemes. In addition, for each identified CAPTCHA category, the most successful attack methods are summarized by also describing how CAPTCHA schemes evolved to resist bot attacks, and discussing the limitations of different CAPTCHA schemes from the security, usability and compatibility point of view. Finally, an assessment of the open issues, challenges, and opportunities for further study is provided, paving the road toward the design of the next-generation secure and user-friendly CAPTCHA schemes. CCS Concepts: • Security and privacy → Authentication; Graphical / visual passwords.

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Cursor CAPTCHA — Implementing CAPTCHA using mouse cursor

Cited by 7 publications

References 11 publications

CAPTCHaStar! A Novel CAPTCHA Based on Interactive Shape Discovery

CAPTCHaStar! A Novel CAPTCHA Based on Interactive Shape Discovery

Human-artificial intelligence approaches for secure analysis in CAPTCHA codes

Gotta CAPTCHA 'Em All: A Survey of Twenty years of the Human-or-Computer Dilemma

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