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Slider gap positioning and dragging track generation method for slider verification code

A trajectory generation and verification code technology, applied in the Internet field, can solve the problems of repeated trajectory generation, insufficient randomness, and cliff-like decline in the identification pass rate, and achieve the effect of high identification pass rate and strong pass rate stability.

Active Publication Date: 2020-06-19
南京星云数字技术有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The above three trajectory generation methods are mainly based on the fitting of a fixed mathematical model and the retrieval of trajectory databases. The algorithm itself has not really learned the essential characteristics of human behavior of dragging the slider, so there are defects of insufficient randomness and possible repetition of generated trajectories. , especially when the slider verification party uses artificial intelligence and big data technology, the above three methods will have a cliff-like decline in the recognition pass rate as the number of tests increases

Method used

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  • Slider gap positioning and dragging track generation method for slider verification code
  • Slider gap positioning and dragging track generation method for slider verification code
  • Slider gap positioning and dragging track generation method for slider verification code

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Experimental program
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Effect test

Embodiment approach 1

[0222] ① LSTM front-end implementation plan 1

[0223] combine image 3 , the scheme first uses the Burr statistical model to model the relationship between the displacement distance and the dragging time, that is, the model outputs the total time-consuming A that conforms to the Burr model, and the unit is milliseconds.

[0224] Secondly, according to the collected human slider drag data set, the statistical relationship of the time sampling frequency is analyzed, and the model is established to generate the time sampling sequence. In this step, the acceptance-rejection method (Acceptance-RejectionMethod) is first used to select the non-uniform sampling points of the time series, and then for the sampling points that have been determined to be selected, the specific sampling values ​​(in milliseconds) of the sampling points are calculated. Gaussian random number generation with specified mean and variance. This step will finally generate a non-uniform sampling time series (...

Embodiment approach 2

[0226] This implementation uses a layer of LSTM to directly establish a model from input (displacement distance, time-consuming) to output non-uniform sampling time series. All the time step output of the LSTM layer will be used as the input of the LSTM backend, and the number of time steps is 55, and the number of time steps is filled with zeros.

[0227] combine image 3, the LSTM regression model is written using the Tensorflow framework. Its network architecture and loss function have been customized and modified to adapt to the generation of variable-length slider drag trajectories with different displacement distances and different drag durations in the application of slider verification code recognition. First, the input of the LSTM model is forced into the actual time series length, and the output is the full time step output; second, a dropout layer is added before the output layer is connected. The dropout layer and the output layer use fully connected (fully connec...

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Abstract

The invention discloses a slider gap positioning and dragging track generation method for a slider verification code. The method comprises two parts: offline model training: constructing a target detection model based on deep learning, calculating the coordinates of the central position of a slider notch according to a background picture of the slider notch, marking a rectangular marking frame, and carrying out supervised target detection model training; and establishing an LSTM front-end model and an LSTM rear-end regression model according to the real person sliding block dragging track, andperforming model training. Carrying out online model service: carrying out screenshot on the screen, and processing to obtain a background picture containing a sliding block picture and a sliding block notch; taking the slider notch background picture as input, and executing the trained target detection model to obtain a slider notch center position coordinate; and executing the trained LSTM front-end model and the LSTM rear-end regression model to obtain a sliding block dragging track time sequence. According to the method, random generation of the anthropomorphic slider track is realized, the recognition passing rate is high, and the passing rate stability is high.

Description

technical field [0001] The invention relates to the technical field of the Internet, in particular to a slider gap positioning and drag trajectory generation method of a slider verification code. Background technique [0002] Captcha is a kind of Turing test for determining whether an operation is performed on a human. In the field of software testing, testers use software to conduct automated testing of websites, rapid release of information, and automated transactions. Automatic identification of verification codes can help improve testing efficiency and test coverage, reduce manual testing links, and avoid human errors. The slider verification code is a specific implementation form of the Turing test, and its implementation type is called Dynamic CognitiveGame. At present, the industry is researching a complete end-to-end slider verification code automatic identification technology, which includes research on the system architecture level and algorithm level. At the alg...

Claims

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Application Information

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IPC IPC(8): G06F3/0484G06F21/36G06N3/04G06N3/08
CPCG06F3/04847G06F21/36G06N3/084G06N3/045G06N3/044
Inventor 王元
Owner 南京星云数字技术有限公司