The Experts below are selected from a list of 2322 Experts worldwide ranked by ideXlab platform

Alessia Amelio - One of the best experts on this subject based on the ideXlab platform.

  • Exploring the usability of the text-based CAPTCHA on tablet computers
    2019
    Co-Authors: Darko Brodić, Alessia Amelio
    Abstract:

    This paper analyses and discusses the usability aspect of the text-based CAPTCHA in terms of response time and success in solving the CAPTCHA on tablet computers. The response time is the time spent by the user to find a solution to the CAPTCHA. The analysis is separately conducted on text-based CAPTCHA with only text and numbers. Then, the results are compared and the differences in response time and success in solving the two types of CAPTCHA are underlined. This is accomplished by asking 125 Internet users to solve the text-based CAPTCHA on the tablet computer. Their gender, age, education level, Internet experience, response time and success in solving two types of text-based CAPTCHA are collected in a dataset. Then, advanced statistical analysis by association rule mining is performed. It shows the dependence of the response time and success in solving the CAPTCHA on co-occurrence of gender, age, education level and Internet experience and the strength of this dependence by support, confidence and lift measures. This study provides relevant information for designing new CAPTCHAs which may be more accustomed to specific types of Internet users.

  • analysis of the human computer interaction on the example of image based CAPTCHA by association rule mining
    arXiv: Human-Computer Interaction, 2016
    Co-Authors: Darko Brodic, Alessia Amelio
    Abstract:

    The paper analyzes the interaction between humans and computers in terms of response time in solving the image-based CAPTCHA. In particular, the analysis focuses on the attitude of the different Internet users in easily solving four different types of image-based CAPTCHAs which include facial expressions like: animated character, old woman, surprised face, worried face. To pursue this goal, an experiment is realized involving 100 Internet users in solving the four types of CAPTCHAs, differentiated by age, Internet experience, and education level. The response times are collected for each user. Then, association rules are extracted from user data, for evaluating the dependence of the response time in solving the CAPTCHA from age, education level and experience in internet usage by statistical analysis. The results implicitly capture the users' psychological states showing in what states the users are more sensible. It reveals to be a novelty and a meaningful analysis in the state-of-the-art.

  • analysis of the human computer interaction on the example of image based CAPTCHA by association rule mining
    International Workshop on Symbiotic Interaction, 2016
    Co-Authors: Darko Brodic, Alessia Amelio
    Abstract:

    The paper analyzes the interaction between humans and computers in terms of response time in solving the image-based CAPT-CHA. In particular, the analysis focuses on the attitude of the different Internet users in easily solving four different types of image-based CAPTCHAs which include facial expressions such as: animated character, old woman, surprised face, worried face. To pursue this goal, an experiment is realized involving 100 Internet users in solving the four types of CAPTCHAs, differentiated by age, Internet experience, and education level. The response times are collected for each user. Then, association rules are extracted from user data, for evaluating the dependence of the response time in solving the CAPTCHA from age, education level and experience in Internet usage by statistical analysis. The results implicitly capture the users’ psychological states showing in what states the users are more sensitive. It reveals to be a novelty and a meaningful analysis in the state-of-the-art.

Jeff Yan - One of the best experts on this subject based on the ideXlab platform.

  • bot cyborg and automated turing test
    International Workshop on Security, 2009
    Co-Authors: Jeff Yan
    Abstract:

    Ross Anderson: Bot tending might be an attractive activity for children, because children could receive the challenges on their mobile phones, to which they are almost physiologically attached these days, and they're perhaps used to relatively smaller amounts of pocket money. Mike Bond: You talked about routes for sending CAPTCHAs which go outside the game; given that the bot has control of the client, what about sending the CAPTCHA back into the game to a human player who is maybe indifferent about bots, and then paying him a virtual currency to solve it? The client would have both the infrastructure to reinsert the CAPTCHA, and to make a payment, there and then.

  • usability of CAPTCHAs or usability issues in CAPTCHA design
    Symposium On Usable Privacy and Security, 2008
    Co-Authors: Jeff Yan, Ahmad Salah El Ahmad
    Abstract:

    CAPTCHA is now almost a standard security technology, and has found widespread application in commercial websites. Usability and robustness are two fundamental issues with CAPTCHA, and they often interconnect with each other. This paper discusses usability issues that should be considered and addressed in the design of CAPTCHAs. Some of these issues are intuitive, but some others have subtle implications for robustness (or security). A simple but novel framework for examining CAPTCHA usability is also proposed.

  • breaking visual CAPTCHAs with naive pattern recognition algorithms
    Annual Computer Security Applications Conference, 2007
    Co-Authors: Jeff Yan, A El S Ahmad
    Abstract:

    Visual CAPTCHAs have been widely used across the Internet to defend against undesirable or malicious bot programs. In this paper, we document how we have broken most such visual schemes provided at CAPTCHAservice.org, a publicly available web service for CAPTCHA generation. These schemes were effectively resistant to attacks conducted using a high-quality Optical Character Recognition program, but were broken with a near 100% success rate by our novel attacks. In contrast to early work that relied on sophisticated computer vision or machine learning algorithms, we used simple pattern recognition algorithms but exploited fatal design errors that we discovered in each scheme. Surprisingly, our simple attacks can also break many other schemes deployed on the Internet at the time of writing: their design had similar errors. We also discuss defence against our attacks and new insights on the design of visual CAPTCHA schemes.

P C Van Oorschot - One of the best experts on this subject based on the ideXlab platform.

  • security analysis and related usability of motion based CAPTCHAs decoding codewords in motion
    IEEE Transactions on Dependable and Secure Computing, 2014
    Co-Authors: Gerardo Reynaga, Sonia Chiasson, Fabian Monrose, Janmichael Frahm, P C Van Oorschot
    Abstract:

    We explore the robustness and usability of moving-image object recognition (video) CAPTCHAs, designing and implementing automated attacks based on computer vision techniques. Our approach is suitable for broad classes of moving-image CAPTCHAs involving rigid objects. We first present an attack that defeats instances of such a CAPTCHA (NuCAPTCHA) representing the state-of-the-art, involving dynamic text strings called codewords. We then consider design modifications to mitigate the attacks (e.g., overlapping characters more closely, randomly changing the font of individual characters, or even randomly varying the number of characters in the codeword). We implement the modified CAPTCHAs and test if designs modified for greater robustness maintain usability. Our lab-based studies show that the modified CAPTCHAs fail to offer viable usability, even when the CAPTCHA strength is reduced below acceptable targets. Worse yet, our GPU-based implementation shows that our automated approach can decode these CAPTCHAs faster than humans can, and we can do so at a relatively low cost of roughly 50 cents per 1,000 CAPTCHAs solved based on Amazon EC2 rates circa 2012. To further demonstrate the challenges in designing usable CAPTCHAs, we also implement and test another variant of moving text strings using the known emerging images concept. This variant is resilient to our attacks and also offers similar usability to commercially available approaches. We explain why fundamental elements of the emerging images idea resist our current attack where others fail.

  • security and usability challenges of moving object CAPTCHAs decoding codewords in motion
    USENIX Security Symposium, 2012
    Co-Authors: Gerardo Reynaga, Sonia Chiasson, Jm Frahm, Fabian Monrose, P C Van Oorschot
    Abstract:

    We explore the robustness and usability of moving-image object recognition (video) CAPTCHAs, designing and implementing automated attacks based on computer vision techniques. Our approach is suitable for broad classes of moving-image CAPTCHAs involving rigid objects. We first present an attack that defeats instances of such a CAPTCHA (NuCAPTCHA) representing the state-of-the-art, involving dynamic text strings called codewords. We then consider design modifications to mitigate the attacks (e.g., overlapping characters more closely). We implement the modified CAPTCHAs and test if designs modified for greater robustness maintain usability. Our lab-based studies show that the modified CAPTCHAs fail to offer viable usability, even when the CAPTCHA strength is reduced below acceptable targets--signaling that the modified designs are not viable. We also implement and test another variant of moving text strings using the known emerging images idea. This variant is resilient to our attacks and also offers similar usability to commercially available approaches. We explain why fundamental elements of the emerging images concept resist our current attack where others fails.

Bostik Ondrej - One of the best experts on this subject based on the ideXlab platform.

  • Semi-Supervised Approach To Train CAPTCHA Letter Position Detetor
    Vysoké učení technické v Brně Fakulta elektrotechniky a komunikačních technologií, 2021
    Co-Authors: Bostik Ondrej
    Abstract:

    Common Optical Character Recognition (OCR) methods benefit from the fact, that the text is distributedin images in a predictable pattern. This is not the situation with CAPTCHA systems. UtilizingOCR algorithms to overcome common web anti-abuse CAPTCHA systems is therefore a challengingtask. To train a system to overcome any CAPTCHA scheme, an attacker needs a huge dataset ofannotated images. And for some methods, the attacker needs not only the right answers but also anexact position of the character in the CAPTCHA image.Annotate the positions of the object in an image is a time-consuming task. In this paper, we proposea system, which can help to annotate the position of CAPTCHA character with minimal humaninteraction. After annotating a small sample of targeted CAPTCHA images, a YOLO-based regiondetection deep network is used to search for the characters’ locations

  • Semi-Supervised Deep Learning Approach For Breaking Geocaching CAPTCHAs
    Vysoké učení technické v Brně Fakulta elektrotechniky a komunikačních technologií, 2020
    Co-Authors: Bostik Ondrej
    Abstract:

    For nearly two decades, a substantial part of developed anti-abuse and anti-spam systems for web applications called CAPTCHA is based on imperfections in OCR (Optical Character Recognition) algorithms. But with improvements in Deep Learning in OCR, these systems are now obsolete. More and more systems can now break various text CAPTCHAs with great accuracy. Now with sufficient training dataset, almost every text-based CAPTCHA scheme can be broken. The focus of this work is to present an idea of a semi-supervised method for reading text-based CAPTCHA which needs only a small initial dataset. The main part of this article is dealing with the problem of training a deep learning system with only a small sample of target CAPTCHA scheme via transfer learning

Ye Wang - One of the best experts on this subject based on the ideXlab platform.

  • AN ADVANCED FRAMEWORK TO OPTIMIZE MULTI-RESIDUAL RECURRENT NEURAL NETWORK FOR BETTER SEQUENCE LEARNING
    2020
    Co-Authors: Ye Wang
    Abstract:

    Sequence Learning is the cornerstone of data mining, and is significant in extracting useful information, from sequencing sounds in a speech to sequencing semantics in linguistics. Before sequence learning, finding a proper approach to collect sequential data is essential. The main purpose of the proposed recognition system is to defeat the CAPTCHA (Completely Automated Public Turing test to Tell Computers and Humans Apart) because we need to collect the data for the sequence learning. Besides, defeating the CAPTCHAs is also beneficial to improving the safety when we expose the CAPTCHAs’ deficiency. As an effective way to protect the security and preserve the privacy of the network data, CAPTCHA is widely used in recent years. Normally, three steps are utilized to defeat the CHAPCHAs - Preprocessing, Segmentation and Recognition. Since there is not a universal segmentation framework that is adaptive to all the possible CAPTCHA characters, each individual character requires separate segmentation which makes the segmentation complicated. In this dissertation, we present a self-adaptive algorithm in optimally segmenting different CAPTCHA characters. Current classifiers including Template Matching (TM), Optical Character Recognition (OCR) and Convolutional Neural Networks (CNN) are utilized in classifying these segmented CAPTCHA characters. The CAPTCHAs experimental results show the outperformance of the proposed recognition system in defeating the CAPTCHA. In the currently existing financial related Chinese text classification task, the data quality of those tasks is not ideal because labeled Chinese datasets are not large enough. Besides the textbased CAPTCHA recognition, short-term text classification also plays an important role in sequence learning. After obtaining the titles of Chinese commercial news, a new Chinese financial related Short-term Text Classification Task (STCT) is introduced and its corresponding benchmark is provided. As a popular solution for STCT in sequence learning, recurrent neural networks (RNNs) have proven its efficiency in processing sequential information. However, the traditional RNNs have suffered from the gradient diminishing problem until the advent of Long Short-Term Memory (LSTM). The LSTM, though, is still weak in capturing long-time dependency in sequential data due to the inadequacy of memory capacity in LSTM cells. To address this challenge, we propose an Attention-augmentation Bidirectional Multi-residual Recurrent Neural Network (ABMRNN) to overcome this deficiency. The proposed ABMRNN integrates both past and future information at every time step with an omniscient attention model. The multi-residual mechanism has also been proposed in our model targeting the pattern of the relationship between the current time step and further distant time steps instead of only one previous time step. The experimental results show that the proposed model outperforms the traditional statistical classifiers and other state-of-theart variations of RNN architectures in both the STCT and other public tasks, such as AG news, Sequential-MNIST, and IMDB

  • An optimized system to solve text-based CAPTCHA
    2018
    Co-Authors: Ye Wang
    Abstract:

    CAPTCHA(Completely Automated Public Turing test to Tell Computers and Humans Apart) can be used to protect data from auto bots. Countless kinds of CAPTCHAs are thus designed, while we most frequently utilize text-based scheme because of most convenience and user-friendly way \cite{bursztein2011text}. Currently, various types of CAPTCHAs need corresponding segmentation to identify single character due to the numerous different segmentation ways. Our goal is to defeat the CAPTCHA, thus firstly the CAPTCHAs need to be split into character by character. There isn't a regular segmentation algorithm to obtain the divided characters in all kinds of examples, which means that we have to treat the segmentation individually. In this paper, we build a whole system to defeat the CAPTCHAs as well as achieve state-of-the-art performance. In detail, we present our self-adaptive algorithm to segment different kinds of characters optimally, and then utilize both the existing methods and our own constructed convolutional neural network as an extra classifier. Results are provided showing how our system work well towards defeating these CAPTCHAs

  • An Optimized System to Solve Text-Based CAPTCHA
    2018
    Co-Authors: Ye Wang
    Abstract:

    CAPTCHA(Completely Automated Public Turing test to Tell Computers and Humans Apart) can be used to protect data from auto bots. Countless kinds of CAPTCHAs are thus designed, while we most frequently utilize text-based scheme because of most convenience and user-friendly way [1]. Currently, various types of CAPTCHAs need corresponding segmentation to identify single character due to the numerous different segmentation ways. Our goal is to defeat the CAPTCHA,thus rstly the CAPTCHAs need to be split into character by character. There isn't a regular segmentation algorithm to obtain the divided characters in all kinds of examples, which means that we have to treat the segmentation individually. In this paper, we build a whole system todefeat the CAPTCHAs as well as achieve state-of-the-art performance.In detail, we present our self-adaptive algorithm to segment different kinds of characters optimally, and then utilize both the existing methods and our own constructed convolutional neural network as an extra classfier. Results are provided showing how our system work well towards defeating these CAPTCHAs