Deep-CAPTCHA: a deep learning based CAPTCHA solver for vulnerability
assessment
- URL: http://arxiv.org/abs/2006.08296v2
- Date: Wed, 24 Jun 2020 19:55:33 GMT
- Title: Deep-CAPTCHA: a deep learning based CAPTCHA solver for vulnerability
assessment
- Authors: Zahra Noury and Mahdi Rezaei
- Abstract summary: This research investigates the weaknesses and vulnerabilities of the CAPTCHA generator systems.
We develop a Convolutional Neural Network called Deep-CAPTCHA to achieve this goal.
Our network's cracking accuracy leads to a high rate of 98.94% and 98.31% for the numerical and the alpha-numerical test datasets.
- Score: 1.027974860479791
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: CAPTCHA is a human-centred test to distinguish a human operator from bots,
attacking programs, or other computerised agents that tries to imitate human
intelligence. In this research, we investigate a way to crack visual CAPTCHA
tests by an automated deep learning based solution. The goal of this research
is to investigate the weaknesses and vulnerabilities of the CAPTCHA generator
systems; hence, developing more robust CAPTCHAs, without taking the risks of
manual try and fail efforts. We develop a Convolutional Neural Network called
Deep-CAPTCHA to achieve this goal. The proposed platform is able to investigate
both numerical and alphanumerical CAPTCHAs. To train and develop an efficient
model, we have generated a dataset of 500,000 CAPTCHAs to train our model. In
this paper, we present our customised deep neural network model, we review the
research gaps, the existing challenges, and the solutions to cope with the
issues. Our network's cracking accuracy leads to a high rate of 98.94% and
98.31% for the numerical and the alpha-numerical test datasets, respectively.
That means more works is required to develop robust CAPTCHAs, to be
non-crackable against automated artificial agents. As the outcome of this
research, we identify some efficient techniques to improve the security of the
CAPTCHAs, based on the performance analysis conducted on the Deep-CAPTCHA
model.
Related papers
- Affordance-Guided Reinforcement Learning via Visual Prompting [51.361977466993345]
Keypoint-based Affordance Guidance for Improvements (KAGI) is a method leveraging rewards shaped by vision-language models (VLMs) for autonomous RL.
On real-world manipulation tasks specified by natural language descriptions, KAGI improves the sample efficiency of autonomous RL and enables successful task completion in 20K online fine-tuning steps.
arXiv Detail & Related papers (2024-07-14T21:41:29Z) - Oedipus: LLM-enchanced Reasoning CAPTCHA Solver [17.074422329618212]
Oedipus is an innovative end-to-end framework for automated reasoning CAPTCHA solving.
Central to this framework is a novel strategy that dissects the complex and human-easy-AI-hard tasks into a sequence of simpler and AI-easy steps.
Our evaluation shows that Oedipus effectively resolves the studied CAPTCHAs, achieving an average success rate of 63.5%.
arXiv Detail & Related papers (2024-05-13T06:32:57Z) - A Survey of Adversarial CAPTCHAs on its History, Classification and
Generation [69.36242543069123]
We extend the definition of adversarial CAPTCHAs and propose a classification method for adversarial CAPTCHAs.
Also, we analyze some defense methods that can be used to defend adversarial CAPTCHAs, indicating potential threats to adversarial CAPTCHAs.
arXiv Detail & Related papers (2023-11-22T08:44:58Z) - Diff-CAPTCHA: An Image-based CAPTCHA with Security Enhanced by Denoising
Diffusion Model [2.1551899143698328]
Diff-CAPTCHA is an image-click CAPTCHA scheme based on diffusion models.
This paper develops several attack methods, including end-to-end attacks based on Faster R-CNN and two-stage attacks.
Results show that diffusion models can effectively enhance CAPTCHA security while maintaining good usability in human testing.
arXiv Detail & Related papers (2023-08-16T13:41:29Z) - EnSolver: Uncertainty-Aware Ensemble CAPTCHA Solvers with Theoretical Guarantees [1.9649272351760065]
We propose Enr, a family of solvers that use deep ensemble uncertainty to detect and skip out-of-distribution CAPTCHAs.
We prove novel theoretical bounds on the effectiveness of our solvers and demonstrate their use with state-of-the-art CAPTCHA solvers.
arXiv Detail & Related papers (2023-07-27T20:19:11Z) - Vulnerability analysis of captcha using Deep learning [0.0]
This research investigates the flaws and vulnerabilities in the CAPTCHA generating systems.
To achieve this, we created CapNet, a Convolutional Neural Network.
The proposed platform can evaluate both numerical and alphanumerical CAPTCHAs
arXiv Detail & Related papers (2023-02-18T17:45:11Z) - Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks.
We present an adversarial training strategy that mitigates the impact of such simulated attacks.
arXiv Detail & Related papers (2022-03-25T19:57:19Z) - Robust Text CAPTCHAs Using Adversarial Examples [129.29523847765952]
We propose a user-friendly text-based CAPTCHA generation method named Robust Text CAPTCHA (RTC)
At the first stage, the foregrounds and backgrounds are constructed with randomly sampled font and background images.
At the second stage, we apply a highly transferable adversarial attack for text CAPTCHAs to better obstruct CAPTCHA solvers.
arXiv Detail & Related papers (2021-01-07T11:03:07Z) - Automating Botnet Detection with Graph Neural Networks [106.24877728212546]
Botnets are now a major source for many network attacks, such as DDoS attacks and spam.
In this paper, we consider the neural network design challenges of using modern deep learning techniques to learn policies for botnet detection automatically.
arXiv Detail & Related papers (2020-03-13T15:34:33Z) - HYDRA: Pruning Adversarially Robust Neural Networks [58.061681100058316]
Deep learning faces two key challenges: lack of robustness against adversarial attacks and large neural network size.
We propose to make pruning techniques aware of the robust training objective and let the training objective guide the search for which connections to prune.
We demonstrate that our approach, titled HYDRA, achieves compressed networks with state-of-the-art benign and robust accuracy, simultaneously.
arXiv Detail & Related papers (2020-02-24T19:54:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.