Text based CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is the most widely used mechanism adopted by numerous popular web sites in order to differentiate between machines and humans, however due to extensive research carried out by computer vision researchers, it is now a days vulnerable against automated attacks. Segmentation is the most difficult task in automatic recognition of CAPTCHAs, therefore contemporary Text based CAPTCHAs try to combine the characters together in order to make them as segmentation resistant against these attacks as possible. In this research, we have found vulnerabilities in such CAPTCHAs, a novel mechanism, i.e. the recognition based segmentation is applied to crop such connected characters, a sliding window based neural network classifier is used to recognize and segment the connected characters. Experimental results have proved 95.5% recognition success rate and 58.25% segmentation success rate on our dataset of tmall CAPTCHAs, this algorithm is further tested on two other datasets of slightly different implementations and promising results were achieved.

Recognition based segmentation of connected characters in text based CAPTCHAs

Websites can increase their security and prevent harmful Internet attacks by providing CAPTCHA verification for determining whether end-user is a human or a robot. Text-based CAPTCHA is the most common and designed to be easily recognized by humans and difficult to identify by machines or robots. However, with the dramatic advancements in deep learning, it becomes much easier to build convolutional neural network (CNN) models that can efficiently recognize text-based CAPTCHAs. In this study, we introduce an efficient CNN model that uses attached binary images to recognize CAPTCHAs. By making a specific number of copies of the input CAPTCHA image equal to the number of characters in that input CAPTCHA image and attaching distinct binary images to each copy, we build a new CNN model that can recognize CAPTCHAs effectively. The model has a simple structure and small storage size and does not require the segmentation of CAPTCHAs into individual characters. After training and testing the proposed CAPTCHA recognition CNN model, the achieved experimental results reveal the strength of the model in CAPTCHA character recognition.

https://www.mdpi.com/2079-9292/9/9/1522/pdf

CAPTCHA Recognition Using Deep Learning with Attached Binary Images

Systems and methods of determining image characteristics are provided. More particularly, a first image having an unknown characteristic can be obtained. The first image can be provided to a plurality of user devices in a verification challenge. The verification challenge can include one or more instructions to be presented to a user of each user device. The instructions being determined based at least in part on the first image. User responses can be received, and an unknown characteristic of the first image can be determined based at least in part on the received responses. Subsequent to determining the unknown characteristic of the first image, one or more machine learning models can be trained based at least in part on the determined characteristic.

Image based CAPTCHA challenges

Cybersecurity practitioners generate a Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHAs) as a form of security mechanism in website applications, in order to differentiate between human end-users and machine bots. They tend to use standard security to implement CAPTCHAs in order to prevent hackers from writing malicious automated programs to make false website registrations and to restrict them from stealing end-users’ private information. Among the categories of CAPTCHAs, the text-based CAPTCHA is the most widely used. However, with the evolution of deep learning, it has been so dramatic that tasks previously thought not easily addressable by computers and used as CAPTCHA to prevent spam are now possible to break. The workflow of CAPTCHA breaking is a combination of efforts, approaches, and the development of the computation-efficient Convolutional Neural Network (CNN) model that attempts to increase accuracy. In this study, in contrast to breaking the whole CAPTCHA images simultaneously, this study split four-character CAPTCHA images for the individual characters with a 2-pixel margin around the edges of a new training dataset, and then proposed an efficient and accurate Depth-wise Separable Convolutional Neural Network for breaking text-based CAPTCHAs. Most importantly, to the best of our knowledge, this is the first CAPTCHA breaking study to use the Depth-wise Separable Convolution layer to build an efficient CNN model to break text-based CAPTCHAs. We have evaluated and compared the performance of our proposed model to that of fine-tuning other popular CNN image recognition architectures on the generated CAPTCHA image dataset. In real-time, our proposed model used less time to break the text-based CAPTCHAs with an accuracy of more than 99% on the testing dataset. We observed that our proposed CNN model has efficiently improved the CAPTCHA breaking accuracy and streamlined the structure of the CAPTCHA breaking network as compared to other CAPTCHA breaking techniques.

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An Efficient and Accurate Depth-Wise Separable Convolutional Neural Network for Cybersecurity Vulnerability Assessment Based on CAPTCHA Breaking

The chest computed tomography (CT) is the most commonly used imaging technique for the inspection of lung lesions. In order to provide the physician more valuable preoperative opinions, a powerful computer-aided diagnostic (CAD) system is indispensable. In this paper, we aim to develop an ant colony optimization (ACO-based) classifier to extract the lung mass. We could calculate some information such as its boundary, precise size, localization of tumors, and spatial relations. Final, we reconstructed the extracted lung and tumor regions to a 3D volume module to provide physicians the more reliable vision. In order to validate the proposed system, we have tested our method in a database from 15 lung patients. We also demonstrated the accuracy of the segmentation method using some power statistical protocols. The experiments indicate our method results more satisfied performance in most cases, and can help investigators detect lung lesion for further examination.

Image Segmentation for Lung Lesions Using Ant Colony Optimization Classifier in Chest CT

Through most of captcha images all said that their system can defense the external attacking, many corresponding researches are still presented to examine the captcha systems in the current internet platforms. The safety of captcha image can be decided by the complexity of imaging structures. Therefore, different captcha recognition methods are proposed to apply into different captcha images. In this study, there are many noisy lines and points in our testing captcha cases. We proposed the probability pattern framework to recognize the target numbers in the captcha images. In the experiment, the quantitative assessment shows that the proposed recognition method achieved an average of 81.05% for more than two thousand captcha cases.

A Study on Captcha Recognition

Chest computed tomography (CT) is the most commonly used technique for the inspection of lung lesions. However, the lobe fissures in lung CT is still difficult to observe owing to its imaging structure. Therefore, in this paper, we aimed to develop an efficient tracking framework to extract the lobe fissures by the proposed modified ant colony optimization (ACO) algorithm. We used the method of increasing the consistency of pheromone on lobe fissure to improve the accuracy of path tracking. In order to validate the proposed system, we had tested our method in a database from 15 lung patients. In the experiment, the quantitative assessment shows that the proposed ACO method achieved the average F-measures of 80.9% and 82.84% in left and right lungs, respectively. The experiments indicate our method results more satisfied performance, and can help investigators detect lung lesion for further examination.

/pdf/the-lobe-fissure-tracking-by-the-modified-ant-colony-27fqkgf920.pdf

The lobe fissure tracking by the modified ant colony optimization framework in CT images

This paper proposes an novel idea to develop application. Different from traditional programming methods, users can design their application without long term training. The tool prevents syntax and semantic errors during design the application. Based on the proposed idea a total solution to develop application for android system is established. The system includes a cloud system, user management system, web base development tool and an auto deploy system. By means of simply drag and drop, users can design and deploy their application. The architecture also provides flexibility to change the application target with modify the language layer.

Web Base Android Application Development System

The chest computed tomography (CT) is the most commonly used imaging technique for the inspection of lung lesions. In order to provide the physician more valuable preoperative opinions, a powerful computer-aided diagnostic (CAD) system is indispensable. In this paper, we aim to develop an efficient tracking framework to extract the lobe fissures by the proposed modified ant colony optimization (ACO) algorithm. In this procedure, we will increase the consistency of pheromones on the lobe fissure. Hence, the gray-value of lobe fissure will be improved, and it may promote the accuracy of segmentation procedure. In order to validate the proposed system, we have tested our method in a database from 15 lung patients. The experiments indicate our method results more satisfied performance in most cases, and can help investigators detect lung lesion for further examination.

Chii Jen Chen

Papers

A Study on Captcha Recognition

The lobe fissure tracking by the modified ant colony optimization framework in CT images

Web Base Android Application Development System

The Tracking Framework for Lobe Fissure Based on the Modified Ant Colony Optimization Algorithm