Is image-based CAPTCHA secure against attacks based on machine learning? An experimental study

Alqahtani, Fatmah H.; Alsulaiman, Fawaz A.

doi:10.1016/j.cose.2019.101635

Cited by 31 publications

(18 citation statements)

References 19 publications

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“…[4] were the first to use AI hard problems in information security. They proved that "any program that passes the tests generated by a CAPTCHA can be used to solve any AI hard problem" [3]- [5].…”

Section: Figure 1: Examples Of Captcha Challengesmentioning

confidence: 99%

“…1) The CAPTCHA AI hard problem CAPTCHA is an information security algorithm that creates challenges that are easy for most humans but hard and unsolvable for bots [3]. Its security is based on the hardness assumption of AI hard problems.…”

Section: The Security Proofmentioning

confidence: 99%

“…Its security is based on the hardness assumption of AI hard problems. A bot that solves a challenge C generated by CAPTCHA can be used to solve these AI hard problems [3]. The definition of CAPTCHA is modified here by elevating the restriction of being easy for humans.…”

Section: The Security Proofmentioning

confidence: 99%

“…An AI hard problem is a problem that needs a computer to be "humanly" smart. This will be a challenge even for quantum computing [3].…”

Section: Introductionmentioning

confidence: 99%

“…An application of AI hard problems in cryptography is CAPTCHA [3]. A CAPTCHA is a program that distinguishes a human from a bot by creating a challenge that is easy for humans but hard for bots.…”

Section: Introductionmentioning

confidence: 99%

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A Password-Based Authentication System Based on the CAPTCHA AI Problem

et al. 2020

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Powerful cryptographic systems based on mathematically hard problems are utilized to ensure tighter security for data communication purposes. However, these traditional cryptographic systems are bound to fail in the ensuing era of quantum computing. Thus, Artificial Intelligence (AI) inspired security methods are needed to secure communications in the era of quantum computing. This paper presents a challenge-response password-based authentication system based on the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) AI hard problem. In this system, a server sends a challenge text to a client, then the client generates a random image and blends the challenge text inside this random image using his password. Then the client sends the generated image to the server. The server extracts the challenge text from the sent image using his copy of the client's password. If the extracted challenge text is the same as the sent challenge text, then both the client's and the server's copies of the password match and the client is authenticated. The efficiency of the proposed system is analyzed and the outcomes prove that the proposed system is efficient in terms of time and space. Also, a security investigation of the proposed system is employed, and the results prove that the system is probabilistic and very sensitive to changes in its parameters. It does not leak any statistical information about the client's password and the generated images cannot be distinguished from random images. In addition, the security of the proposed system is analyzed against two possible attacks; the brute force attack and the replay attack and the results prove that the proposed system is immune to these attacks. Finally, the proposed system is ensured to be indistinguishably secure against an adaptive chosen-challenge text attack (IND-ACCTA), based on the CAPTCHA AI hard problem when the hash function H is modeled as a random oracle.

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Section: Figure 1: Examples Of Captcha Challengesmentioning

confidence: 99%

Section: The Security Proofmentioning

confidence: 99%

Section: The Security Proofmentioning

confidence: 99%

“…An AI hard problem is a problem that needs a computer to be "humanly" smart. This will be a challenge even for quantum computing [3].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Password-Based Authentication System Based on the CAPTCHA AI Problem

et al. 2020

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Improving data quality in online parenting research

Kaylor‐Tapscott,

Tolliver‐Lynn,

Sullivan

2024

Infant and Child Development

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Online surveys provide many benefits for researchers but are vulnerable to fraudulent responders that threaten data quality. We aimed to evaluate the effectiveness of current detection strategies after noting patterns in our data. We systematically reviewed responses (N = 180) from parenting studies utilizing two online platforms. Respondents were categorized as accepted (n = 45) or fraudulent (n = 135), and responses on standardized measures were examined to determine if fraudulent scores differed. Accepted participants were predominately female (98.4%) and White (67.9%). Average caregiver age was 40.61 (SD = 7.37) on Qualtrics and 59.88 (SD = 6.01) on REDCap. The majority of fraudulent responders reported being female (62.1%) and White (80.6%), with average caregiver ages of 37.83 (SD = 7.35) for Qualtrics and 56.16 (SD = 8.86) for REDCap. Fraudulent responders had significantly higher median scores on measures of caregiver distress (rDASS = 0.21) and child behavioural problems (rCBCL = 0.33). Previously recommended strategies were insufficient at preventing and identifying fraudulent responders. Recommendations for improving these strategies are discussed.Highlights The current paper includes experiences using multiple strategies to detect fraudulent responders in two online parenting studies. Improvements to previously recommended strategies and an example review form are included to aid researchers in detecting fraudulent responders.

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