Sequenced Tagged Captcha: Generation and its Analysis

Abstract-CAPTCHAs are employed as a security measure to differentiate human users from bots. A new sound-based CAPTCHA is proposed in this paper, which exploits the gaps between human voice and synthetic voice rather than relays on the auditory perception of human. The user is required to read out a given sentence, which is selected randomly from a specified book. The generated audio file will be analyzed automatically to judge whether the user is a human or not. In this paper, the design of the new CAPTCHA, the analysis of the audio files, and the choice of the audio frame window function are described in detail. And also, some experiments are conducted to fix the critical threshold and the coefficients of three indicators to ensure the security. The proposed audio CAPTCHA is proved accessible to users. The user study has shown that the human success rate reaches approximately 97% and the pass rate of attack software using Microsoft SDK 5.1 is only 4%. The experiments also indicated that it could be solved by most human users in less than 14 seconds and the average time is only 7.8 seconds.

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“…As a matter of the fact, a number of text-based CAPTCHAs used previously on sites such as Yahoo! and Google have already been broken [2].…”

Section: Related Workmentioning

confidence: 99%

“…The other kind offers sounds related with images. Current sound-based CAPTHAs have been broken by high-quality voice recognition and noise removal programs [2]. Some existing audio CAPTCHA is highly error prone and time consuming [3].…”

Section: Captcha Stands For Completely Automated Public Turing Test Tmentioning

confidence: 99%

An Audio CAPTCHA to Distinguish Humans from Computers

et al. 2010

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“…Gupta et al [6] introduced an OCR-based CAPTCHA generation scheme called Sequenced Tagged Captcha (refer Figure 3) which incorporated Tagging. In this, the characters of the CAPTCHA have a Tag (or embedded number) associated with them.…”

Section: Research Work Conducted In Ocr-based Captchasmentioning

confidence: 99%

“…The essence of a CAPTCHA is that it should be identified easily by a human but not by a bot. Broadly, CAPTCHAs can be classified into two groups -OCR-based CAPTCHAs and Non-OCR based CAPTCHAs [6].…”

Section: Introductionmentioning

confidence: 99%

Picture CAPTCHAsWith Sequencing: Their Types and Analysis

Raj¹,

Jain²,

Pahwa³

et al. 2010

IJDS

Self Cite

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“…For example, an early test in 2003 showed that the distorted sound in an audio CAPTCHA that was deployed at Microsoft's Hotmail service was unintelligible to all (four) journalists, with good hearing, that were tested [27]. Current audio-based CAPTCHAs have been broken by highquality voice recognition and noise removal programs [28]. Some existing audio CAPTCHA is highly error prone and time consuming [29].…”

Section: Figure 2: Text-based Captchas Examples (From Various Websites)mentioning

confidence: 99%

Design and Comparison of Advanced Color based Image CAPTCHAs

Kumar¹,

Dhir²

2013

IJCA

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CAPTCHA is a technology which has its base in a test called the Turing Test. Alan Turing, proposed this test as a way to examine whether or not machines can think or appear to think like humans. The main purpose of a CAPTCHA is to block form submissions from spam bots-that is automated scripts. Various types of CAPTCHAs are used, which mostly requires users to enter the strings of characters that appear in distorted form on the screen. These types of distorted stings are unable to understand by bots but human can. The CAPTCHA types are either text based or image based. In this paper, a new color based CAPTCHA is described, which provides color based images to human and human will answer to interrogator with color name or so on the question asked during turing test. These colored images can have single color image, more than one color image or it can have images with objects (like monitor, car, flower etc). For these types of questions, the computer machine will be unable to answer and it means unable to break CAPTCHA. This paper describes in detail the proposed CAPTCHA technology principle, method of implementation, variations and comparison of the accuracy rates. We conducted various experiments to measure the viability and usability of this CAPTCHA approach. An accuracy of 100%, 95% and 90% is observed with single color, multi color and color image based CAPTCHAs respectively.

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Sequenced Tagged Captcha: Generation and its Analysis

Cited by 19 publications

References 6 publications

An Audio CAPTCHA to Distinguish Humans from Computers

An Audio CAPTCHA to Distinguish Humans from Computers

Picture CAPTCHAsWith Sequencing: Their Types and Analysis

Design and Comparison of Advanced Color based Image CAPTCHAs

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