Capture the Bot: Using Adversarial Examples to Improve CAPTCHA Robustness to Bot Attacks

Hitaj, Dorjan; Hitaj, Briland; Jajodia, Sushil; Mancini, Luigi V.

doi:10.1109/mis.2020.3036156

Cited by 9 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results showed that the proposed approach can serve as a key enabler for generating robust CAPTCHAs in practice. Also, a novel CAPTCHA scheme based on adversarial examples is proposed in [18]. Typically, adversarial examples are used to lead an ML model astray.…”

Section: Techniques For Generating Adversarial Captchamentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Adversarial Perturbations and Attacks on CAPTCHAs

Alsuhibany

2023

Applied Sciences

View full text Add to dashboard Cite

The Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) technique has been a topic of interest for several years. The ability of computers to recognize CAPTCHA has significantly increased due to the development of deep learning techniques. To prevent this ability from being utilised, adversarial machine learning has recently been proposed by perturbing CAPTCHA images. As a result of the introduction of various removal methods, this perturbation mechanism can be removed. This paper, thus, presents the first comprehensive survey on adversarial perturbations and attacks on CAPTCHAs. In particular, the art of utilizing deep learning techniques with the aim of breaking CAPTCHAs are reviewed, and the effectiveness of adversarial CAPTCHAs is discussed. Drawing on the reviewed literature, several observations are provided as part of a broader outlook of this research direction. To emphasise adversarial CAPTCHAs as a potential solution for current attacks, a set of perturbation techniques have been suggested for application in adversarial CAPTCHAs.

show abstract

Section: Techniques For Generating Adversarial Captchamentioning

confidence: 99%

“…Most of these works utilize a deep neural network (DNN) model to directly recognize CAPTCHA characters without the segmentation step. To defend against them, several technologies have been proposed, such as in [3][4][5][6][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Adversarial Perturbations and Attacks on CAPTCHAs

Alsuhibany

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Furthermore, approaches such as distortion, background noise mixing, and the use of adversarial instances were proposed as countermeasures against deep learning models. Adversarial examples by Szegedy et al [7] and others have been suggested to enhance its security against ML-based attacks [8][9][10]. However, Na et al [11] suggested a CAPTCHA solver that uses incremental learning on a limited dataset to defeat adversarial CAPTCHAs.…”

Section: Captcha Evolutionmentioning

confidence: 99%

Human-artificial intelligence approaches for secure analysis in CAPTCHA codes

Dinh

Ogiela

2022

EURASIP J. on Info. Security

View full text Add to dashboard Cite

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.

show abstract

“…Furthermore, the authors proposed several countermeasures, including the use of distortion techniques on characters on the background image or in the hint, the addition of noise on background images, and the use of adversarial examples to hinder deep learning models. In this regard, the concept of adversarial examples was first introduced by Szegedy et al [119], and, since then, many researchers proposed CAPTCHA schemes based on adversarial examples to improve its security against ML-based bot attacks [65,98,112]. However, Na et al [85] recently proposed an efficient CAPTCHA solver that breaks adversarial CAPTCHAs using incremental learning with only a small dataset.…”

Section: Evolution Of Captcha Schemesmentioning

confidence: 99%

Gotta CAPTCHA ’Em All: A Survey of 20 Years of the Human-or-computer Dilemma

GuerarMeriem¹,

VerderameLuca²,

Migliardi³

et al. 2021

ACM Comput. Surv.

View full text Add to dashboard Cite

A recent study has found that malicious bots generated nearly a quarter of overall website traffic in 2019 [102]. These malicious bots perform activities such as price and content scraping, account creation and takeover, credit card fraud, denial of service, and so on. 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.

show abstract

Capture the Bot: Using Adversarial Examples to Improve CAPTCHA Robustness to Bot Attacks

Cited by 9 publications

References 8 publications

A Survey on Adversarial Perturbations and Attacks on CAPTCHAs

A Survey on Adversarial Perturbations and Attacks on CAPTCHAs

Human-artificial intelligence approaches for secure analysis in CAPTCHA codes

Gotta CAPTCHA ’Em All: A Survey of 20 Years of the Human-or-computer Dilemma

Contact Info

Product

Resources

About