A slider notch positioning and dragging trajectory generation method for slider verification code

Abstract

The invention discloses a method for positioning the gap of a slider and generating a dragging track of a slider verification code. The method includes two parts, offline model training: building a target detection model based on deep learning, calculating the coordinates of the center position of the slider notch according to the background image of the slider notch, and marking a rectangular label box for supervised target detection model training; The human slider drags the trajectory, establishes the LSTM front-end model and the LSTM back-end regression model, and performs model training. Online model service: Take a screenshot of the screen, process it to obtain the slider picture and the background picture of the slider gap; take the background picture of the slider gap as input, execute the trained target detection model, and obtain the coordinates of the center position of the slider gap; execute The trained LSTM front-end model and the LSTM back-end regression model obtain the time series of the slider drag trajectory. The invention realizes the random generation of the trajectory of the anthropomorphic slider, and has high recognition pass rate and strong pass rate stability.

Description

technical field [0001] The invention relates to the technical field of the Internet, in particular to a method for locating a slider gap and generating a drag trajectory 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 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...

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

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