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Proceedings ArticleDOI

Attack-Resistant aiCAPTCHA Using a Negative Selection Artificial Immune System

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TLDR
Inspired by the negative selection approach in biological immune systems, an innovative two-phase filtering algorithm is proposed which ensures that the CAPTCHA is resilient to automated attack while remaining easy for human users to solve.
Abstract
The growth of online services has resulted in a great need for tools to secure systems from would-be attackers without compromising the user experience. CAPTCHAs (Completely Automated Public Turing Tests to Tell Computers and Humans Apart) are one tool for this purpose, but their popular text-based form has been rendered insecure by improvements in character recognition technology. In this paper, we propose a novel imagebased CAPTCHA which employs object recognition as its test. Inspired by the negative selection approach in biological immune systems, an innovative two-phase filtering algorithm is proposed which ensures that the CAPTCHA is resilient to automated attack while remaining easy for human users to solve. In extensive testing involving over 3,000 participants, the proposed aiCAPTCHA achieved a 92.0% human success rate.

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Citations
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Proceedings ArticleDOI

POSTER: DeepCRACk: Using Deep Learning to Automatically CRack Audio CAPTCHAs

TL;DR: This paper presents a neural network that leverages Mozilla's open source implementation of Baidu's Deep Speech architecture and is currently able to solve the audio version of an open-source CATPCHA system (named SimpleCaptcha), with 98.8% accuracy.
Proceedings ArticleDOI

CAPTCHA: Machine or Human Solvers? A Game-Theoretical Analysis

TL;DR: A game theoretical framework is developed to model the interactions between the defender and the attacker regarding the design and countermeasure of CAPTCHA system and suggests a welfare-improving CAPTCHAs business model by involving decentralized cryptocurrency computation.
Journal ArticleDOI

Predicting the popularity of micro-videos via a feature-discrimination transductive model

TL;DR: A feature-discrimination transductive model is presented that divides the micro-videos into different levels of popularity via the attribute features and predicted the popularity scores via the low-level features precisely, and seeks a latent common feature subspace, where themicro-videos can be comprehensively represented.

An enhanced intrusion detection system using honeypot and captcha techniques

TL;DR: HoneyCAPTCHA, an enhanced intrusion detection framework is designed to solve the above problems as it is capable of detecting crawlers’ attacks, resilient and efficient to users.
Book ChapterDOI

FP-Captcha: An Improved Captcha Design Scheme Based on Face Points

TL;DR: A novel face point based Captcha is proposed, which employs various face points detection as its test, where user will ask to click on correct face points of all human faces presented in the Captcha challenge; which comprises of real and fake face images, with balanced noise and distortions, embedded in a composite background.
References
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Proceedings Article

Very Deep Convolutional Networks for Large-Scale Image Recognition

TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article

Very Deep Convolutional Networks for Large-Scale Image Recognition

TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI

Distinctive Image Features from Scale-Invariant Keypoints

TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Proceedings ArticleDOI

Histograms of oriented gradients for human detection

TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Proceedings ArticleDOI

Object recognition from local scale-invariant features

TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
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