A method for intelligent spinning verification code feature insight and content accurate identification

Abstract

The application relates to the field of content recognition, in particular to a smart rotation verification code feature insight and content accurate recognition method, which screens the smart rotation verification codes that fail to pass the verification, collects aiming track images of the smart rotation verification codes, determines track features of the aiming track images, draws aiming intention maps, labels the aiming intention maps according to the track features, compares the aiming intention maps with a coincidence threshold value and screens the aiming intention maps, retains the smart rotation verification codes that exceed the coincidence threshold value, and carries out secondary verification on the smart rotation verification codes as test verification codes, collects corresponding verification time, compares the verification time with a time threshold value, retains the test verification codes that are lower than the time threshold value and takes the test verification codes as sample verification codes, extracts image quality parameters of the sample verification codes and applies the image quality parameters to all the smart rotation verification codes as sample parameters, and through the combination of image recognition and user behavior analysis, the safety and user experience of the smart rotation verification codes are improved, malicious machine operation is effectively prevented, and the safety of the user's account is protected.

Description

Technical Field

[0001] The present invention relates to the field of content recognition, and in particular to a method for accurately identifying features of smart verification codes and content. Background Art

[0002] Zhixuan's verification code technology relies primarily on image and text recognition. Users need to identify and drag a movable slider within the Zhixuan verification code to distinguish between humans and robots. With the advancement of image recognition technology, simple Zhixuan verification codes have gradually been cracked. Zhixuan's verification code feature insights and precise content recognition methods have evolved, introducing artificial intelligence optimization. By analyzing the user's behavioral data, it determines whether the user is a real person, thereby improving the user experience.

[0003] With the development of artificial intelligence technology, Zhixuan's verification code feature insights and content accurate identification methods are facing new changes and challenges. Its application scope will become wider and its human-computer interaction capabilities will gradually increase.

[0004] Chinese patent application publication number CN110348450A discloses a security assessment method for an image verification code, comprising: obtaining an image verification code to be assessed; performing denoising processing on the image verification code to obtain an image to be identified; processing the image to be identified using an image recognition model to obtain a recognition result; calculating a first degree of match between a target object contained in the image to be identified and the recognition result; and determining the security level of the image verification code to be assessed based on the first degree of match. The disclosure also provides a security assessment device and computer system for an image verification code.

[0005] Chinese patent application publication number CN104125234A discloses a dynamic image security verification method. The method pre-designs a background image, a masked interference element image, and a word library for generating a Zhixuan verification code. A system domain name library for the calling interface is established, and a unique identifier is generated for each domain name. During the verification process, the method first determines whether the user or IP address is banned. If not, a verification image is randomly obtained from the background image, masked interference element image, and the word library, and a verification acquisition record is generated. Upon successful generation of the verification record, the unique identifier and image address for obtaining the Zhixuan verification code are returned. After the user enters the verification information on the verification image and uploads the Zhixuan verification code unique identifier and user input information, the method determines the verification timeliness, whether the verification information is correct, and whether the user's domain name exists, and returns the processing result. This invention can effectively improve the ability to defend against mechanical data scraping, thereby enhancing system security.

[0006] However, the above method has the following problem: it cannot be combined with image recognition to analyze the user's behavior trends. Summary of the Invention

[0007] To this end, the present invention provides a method for accurately identifying the characteristics of smart verification codes and their contents, so as to overcome the problem in the prior art that it is impossible to analyze user behavior trends in combination with image recognition.

[0008] To achieve the above objectives, the present invention provides a method for analyzing the characteristics of smart verification codes and accurately identifying their contents, including:

[0009] Screening the Zhixuan verification code that has not passed the verification and collecting the aiming trajectory image of the Zhixuan verification code, wherein,

[0010] The smart spin verification code includes a fixed background and a movable slider, and the fixed background is provided with a notch position;

[0011] When the movable slider is aligned with the notch, it is determined that the smart rotation verification code has passed the verification;

[0012] The aiming trajectory image is an image of a trajectory of the movable slider being dragged from an initial position to a final position by the user to be verified;

[0013] determining trajectory features of the aiming trajectory image, drawing an aiming intention map, and annotating the aiming intention map according to the trajectory features;

[0014] The aiming intention graph is compared with the overlap threshold and screened, and the smart spin verification codes exceeding the overlap threshold are retained and used as test verification codes for secondary verification, and the corresponding verification time is collected, where:

[0015] The overlap threshold is the minimum standard for the aiming trajectory image to overlap with the correct trajectory;

[0016] Compare the verification time with a time threshold, retain the test verification code below the time threshold and use it as a sample verification code, wherein:

[0017] The verification time is the time required for the user to be verified to complete the dragging process and pass the secondary verification;

[0018] Extract the image quality parameters of the sample verification code and apply them as sample parameters to all smart verification codes, wherein:

[0019] The image quality parameter is the resolution corresponding to the sample verification code.

[0020] Furthermore, the step of collecting the aiming trajectory image of the Zhixuan verification code includes:

[0021] determining an initial position and a final position of the movable slider;

[0022] Determine whether the final position is a gap position, and filter the correct trajectory;

[0023] Drawing the aiming trajectory image according to the dragging record of the movable slider;

[0024] The correct trajectory is the movement trajectory of the movable slider when the user to be verified drags the movable slider from the initial position to the gap position.

[0025] Furthermore, the step of drawing the aiming intention map includes:

[0026] Preprocessing the aiming trajectory image to generate corresponding trajectory preprocessing data;

[0027] The intention learning model learns the trajectory preprocessing data and generates a corresponding aiming intention graph;

[0028] marking the aiming intention graph according to the trajectory characteristics;

[0029] The intention learning model is generated by training a trajectory training set formed by trajectory preprocessing data corresponding to the aiming trajectory image.

[0030] Furthermore, the step of preprocessing the aiming trajectory image includes:

[0031] The trajectory feature is determined according to the aiming trajectory image, wherein:

[0032] The trajectory characteristics include the dragging speed, operation time and / or trajectory direction corresponding to the aiming trajectory image;

[0033] Perform image filtering on the aiming trajectory image to obtain corresponding trajectory filtering data, wherein:

[0034] The image filtering is based on the trajectory characteristics, and removes the aiming trajectory images that do not meet the trajectory characteristics;

[0035] The trajectory filtering data is segmented according to the standard learning rate to form corresponding trajectory preprocessing data, where:

[0036] The standard learning rate is a learning rate that can be recognized by the intentional learning model, and for single learning, the corresponding standard learning rate is a single learning rate.

[0037] Furthermore, the step of comparing the aiming intention graph with the overlap threshold and performing screening includes:

[0038] Obtaining each displayed value in the aiming intention graph;

[0039] The displayed value is compared with the overlap threshold, wherein,

[0040] When the displayed value is lower than the overlap threshold, the corresponding smart rotation verification code is cleared;

[0041] When the displayed value is higher than the overlap threshold, the corresponding Zhixuan verification code is retained and sent to the user to be verified as a test verification code for the secondary verification.

[0042] Furthermore, the steps of collecting verification time include:

[0043] When the user to be verified receives the test verification code, the timer starts timing;

[0044] When the movable slider is aligned with the notch, the timer stops timing and calculates and collects the corresponding verification time;

[0045] Wherein, when the movable slider fails the secondary verification, the timer automatically cancels the timing and resets the time to zero.

[0046] Further, the verification time is compared with the time threshold, wherein,

[0047] The time threshold is the maximum time required for the user to be verified to complete the secondary verification, which is related to the security performance of the Zhixuan verification code.

[0048] Furthermore, when the verification time is lower than the time threshold, the corresponding test verification code is retained and marked as a sample verification code; when the verification time is higher than the time threshold, the corresponding test verification code is cleared and marked as an error verification code.

[0049] Furthermore, obtaining the resolution of the sample verification code, improving the resolution of the sample verification code using image technology, and converting the new resolution into a corresponding image quality parameter;

[0050] Wherein, the resolution includes the height and width of the sample verification code;

[0051] The image technique is to divide a single resolution into several sub-resolutions.

[0052] Furthermore, the image quality parameters are saved and marked as sample parameters, and a setter performs batch image processing and applies the sample parameters to all smart spin verification codes;

[0053] The batch image processing is that the setter traverses all the smart rotation verification codes and updates the corresponding image quality parameters to the sample parameters.

[0054] Compared with the prior art, the present invention screens out the Zhixuan verification codes that have not passed the verification, collects the aiming trajectory images of the Zhixuan verification codes, determines the trajectory features of the aiming trajectory images, draws an aiming intention graph, and marks the aiming intention graph according to the trajectory features, compares the aiming intention graph with the overlap threshold and screens it, retains the Zhixuan verification codes that exceed the overlap threshold and performs secondary verification as test verification codes, collects the corresponding verification time, compares the verification time with the time threshold, retains the test verification codes that are lower than the time threshold and uses them as sample verification codes, extracts the image quality parameters of the sample verification codes, and applies them as sample parameters to all Zhixuan verification codes. By combining image recognition and user behavior analysis, the security and user experience of the Zhixuan verification code are improved, malicious machine operations are effectively prevented, and the user's account security is protected.

[0055] Furthermore, by filtering the correct trajectory and drawing the aiming trajectory image according to the dragging record of the movable slider, the user's behavior trajectory during the verification process can be accurately captured and analyzed, thereby improving the security of the Zhixuan verification code and the accuracy of recognition.

[0056] Furthermore, by preprocessing the aiming trajectory image to generate corresponding trajectory preprocessing data, using the intention learning model to learn the trajectory preprocessing data and generate the corresponding aiming intention graph, it helps to simplify the image data, making it easier to analyze and process, facilitating further analysis of user behavior, and providing feedback during the verification process, thereby improving the accuracy of the intention learning model's predictions.

[0057] Furthermore, by determining the trajectory features of the aiming trajectory image, filtering the aiming trajectory image, and segmenting the filtered trajectory filtered data according to the standard learning rate, trajectory preprocessing data suitable for model learning is formed, and structured data is provided to the intention learning model so that the model can more accurately learn and identify the aiming behavior of the user to be verified, which helps the model to better understand and learn the behavior patterns of the user to be verified, effectively reduce misjudgments, and improve the user experience of the Zhixuan verification code.

[0058] Furthermore, by comparing the aiming intention graph with the overlap threshold and screening it, key data for evaluating the degree of match between user operations and expected trajectories is obtained, which helps to filter out possible automated attacks or erroneous operations, further confirm the user's identity, and ensure that only Zhixuan verification codes that highly match the expected trajectory are used for secondary verification, thereby improving the security and efficiency of the verification process, effectively reducing false positives and missed negatives, and improving the overall effectiveness of the Zhixuan verification code system.

[0059] Furthermore, by calculating and collecting the verification time of the secondary verification and comparing the verification time with the time threshold, retaining the sample verification code and clearing the wrong verification code, the security and accuracy of Zhixuan's verification code are further enhanced. By setting a reasonable time threshold, the system can more effectively identify and distinguish between real users and robot operations, thereby improving the security of Zhixuan's verification code and ensuring that only real users can pass the verification, while automated attacks are effectively blocked.

[0060] Furthermore, by optimizing the image quality of the sample verification code and applying it as a standard parameter to all Zhixuan verification codes, the consistency and high quality of Zhixuan verification codes can be ensured. This not only helps to improve the security of Zhixuan verification codes and reduce the misrecognition rate, but also enhances the user's visual experience, making Zhixuan verification codes easier to be correctly identified and processed. Through batch processing, time and resources can be saved and the efficiency of the Zhixuan verification code management system can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of a method for understanding the characteristics of smart verification codes and accurately identifying their contents.

[0062] Figure 2 A flowchart of the aiming trajectory image for collecting the Zhixuan verification code of the present invention;

[0063] Figure 3 Draw a flow chart of the aiming intention diagram for the present invention;

[0064] Figure 4 This is a flow chart of the present invention for preprocessing the aiming trajectory image. DETAILED DESCRIPTION

[0065] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0066] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0067] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0068] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0069] See also Figure 1 As shown in FIG, it is a flowchart of a method for understanding the characteristics of smart verification codes and accurately identifying the content of the present invention, including:

[0070] Step S1: Screen the verification codes that have not passed the verification and collect the aiming trajectory images of the verification codes.

[0071] SmartSpin verification code includes a fixed background and a movable slider. The fixed background has a gap position.

[0072] When the movable slider is aligned with the notch, the verification code is determined to have passed.

[0073] The aiming trajectory image is a trajectory image of the movable slider being dragged from the initial position to the final position by the user to be verified;

[0074] Step S2, determining the trajectory features of the aiming trajectory image, drawing an aiming intention map, and annotating the aiming intention map according to the trajectory features;

[0075] Step S3: Compare the aiming intention graph with the overlap threshold and filter it, retain the Zhixuan verification code that exceeds the overlap threshold, and use it as a test verification code for secondary verification, and collect the corresponding verification time, where:

[0076] The overlap threshold is the minimum standard for the aiming trajectory image to overlap with the correct trajectory;

[0077] Step S4: compare the verification time with the time threshold, retain the test verification code below the time threshold and use it as a sample verification code, where:

[0078] Verification time is the time required for the user to be verified to complete the dragging process and pass the secondary verification;

[0079] Step S5: extract the image quality parameters of the sample verification code and apply them as sample parameters to all the smart verification codes, where:

[0080] The image quality parameter is the resolution corresponding to the sample verification code.

[0081] By screening the Zhixuan verification codes that have not passed the verification and collecting the aiming trajectory images of the Zhixuan verification codes, determining the trajectory characteristics of the aiming trajectory images, drawing the aiming intention map, and marking the aiming intention map according to the trajectory characteristics, the aiming intention map is compared with the overlap threshold and screened, retaining the Zhixuan verification codes that exceed the overlap threshold and performing secondary verification as test verification codes, collecting the corresponding verification time, comparing the verification time with the time threshold, retaining the test verification codes that are lower than the time threshold and performing secondary verification as sample verification codes, extracting the image quality parameters of the sample verification codes, and applying them as sample parameters to all Zhixuan verification codes. By combining image recognition and user behavior analysis, the security and user experience of the Zhixuan verification code are improved, malicious machine operations are effectively prevented, and the security of users' accounts is protected.

[0082] See also Figure 2 As shown, it is a flow chart of the present invention for collecting the aiming trajectory image of the smart spin verification code, including:

[0083] Step S11, determining the initial position and final position of the movable slider;

[0084] Step S12, determining whether the final position is a gap position and filtering the correct trajectory;

[0085] Step S13, drawing an aiming trajectory image according to the dragging record of the movable slider;

[0086] The correct trajectory is the movement trajectory of the movable slider when the user to be verified drags it from the initial position to the gap position.

[0087] In the specific implementation, we check whether the final position of the movable slider matches the gap position in the smart spin verification code. If the final position is correct (i.e. the movable slider is dragged to the gap position), it is considered a correct track. If not, it is considered an error track and can be filtered out.

[0088] When recording dragging records, all position change data while the user is dragging the slider is collected. This data is used to create an image of the user's dragging trajectory, known as the aiming trajectory image. By recording and analyzing the trajectory of the user dragging the slider, we can capture the user's behavioral patterns during the verification process. Filtering for accurate trajectories helps identify and eliminate automated attacks or abnormal user behavior, as automated scripts often cannot accurately simulate human dragging behavior.

[0089] By filtering the correct trajectory and drawing an aiming trajectory image based on the dragging record of the movable slider, the user's behavior trajectory during the verification process can be accurately captured and analyzed, thereby improving the security of Zhixuan verification code and the accuracy of recognition.

[0090] See also Figure 3 As shown, it is a flow chart of drawing the aiming intention diagram of the present invention, including:

[0091] Step St1, preprocessing the aiming trajectory image to generate corresponding trajectory preprocessing data;

[0092] Step St2: The intention learning model learns the trajectory preprocessing data and generates a corresponding aiming intention graph;

[0093] Step St3, marking the aiming intention graph according to the trajectory characteristics;

[0094] Among them, the intention learning model is generated by training the trajectory training set formed by the trajectory preprocessing data corresponding to the aiming trajectory image.

[0095] In specific implementations, an intention learning model is used to learn from pre-processed trajectory data. This model is generated by training a trajectory training set using a machine learning algorithm, such as a deep learning network. The training set consists of a large number of aiming trajectory images and their corresponding pre-processed trajectory data. Through training, the model learns the characteristics of the trajectory data and can predict the user's aiming intention. The aiming intention map is annotated based on the trajectory characteristics, which involves identifying and marking key points or areas in the aiming intention map for further analysis and verification.

[0096] By preprocessing the aiming trajectory image and generating the corresponding trajectory preprocessing data, the intention learning model is used to learn the trajectory preprocessing data and generate the corresponding aiming intention graph. This helps to simplify the image data, making it easier to analyze and process, facilitate further analysis of user behavior, and provide feedback during the verification process, thereby improving the accuracy of the intention learning model's predictions.

[0097] See also Figure 4 As shown in FIG, it is a flow chart of the present invention for preprocessing the aiming trajectory image, including:

[0098] Step Sp1, determining the trajectory features according to the aiming trajectory image, wherein,

[0099] The trajectory characteristics include the dragging speed, operation time and / or trajectory direction corresponding to the aiming trajectory image;

[0100] Step Sp2, performing image filtering on the aiming trajectory image to obtain corresponding trajectory filtering data, wherein,

[0101] Image filtering is based on trajectory features, removing aiming trajectory images that do not meet the trajectory features;

[0102] Step Sp3, segmenting the trajectory filtering data according to the standard learning rate to form corresponding trajectory preprocessing data, where

[0103] The standard learning rate is a learning rate that the intentional learning model can recognize, and for single-shot learning, the corresponding standard learning rate is a single learning rate.

[0104] In a specific implementation, when the standard learning rate is between 160 and 180 times per second, the intentional learning model has the best analysis effect on trajectory preprocessing data. Preferably, the standard learning rate is 170 times per second.

[0105] The purpose of filtering the aiming trajectory images is to remove those that do not meet specific trajectory characteristics to ensure data quality for subsequent processing. For example, if an image's dragging speed is too fast or too slow, or if the operation time is abnormal, then this image may be filtered out. After filtering, the trajectory filtered data is segmented according to the standard learning rate to form trajectory preprocessing data. The standard learning rate here refers to the learning rate that the intention learning model can recognize and process. For single-shot learning, the corresponding standard learning rate is a single learning rate, which means that the amount of data for each learning session is fixed, so that the model can learn and predict effectively.

[0106] By determining the trajectory features of the aiming trajectory image, filtering the aiming trajectory image, and segmenting the filtered trajectory data according to the standard learning rate, trajectory preprocessing data suitable for model learning is formed. This provides structured data to the intention learning model so that the model can more accurately learn and identify the aiming behavior of the user to be verified. This helps the model better understand and learn the behavioral patterns of the user to be verified, effectively reducing misjudgments and improving the user experience of the Zhixuan verification code.

[0107] Specifically, the steps of comparing the aiming intention map with the coincidence threshold and performing screening include:

[0108] Obtain each displayed value in the aiming intention graph;

[0109] Compare the displayed value to the coincidence threshold, where

[0110] When the displayed value is lower than the overlap threshold, the corresponding Zhixuan verification code will be cleared;

[0111] When the displayed value is higher than the overlap threshold, the corresponding Zhixuan verification code is retained and sent to the user to be verified as a test verification code for secondary verification.

[0112] In some possible implementations, the overlap threshold is set to 80%. If the A value in the aiming intention graph is 70%, which is lower than the overlap threshold, it will be considered that the user's operation does not match the expected trajectory enough, so the corresponding A smart rotation verification code will be cleared, which helps to filter out possible automated attacks or erroneous operations.

[0113] If the B value in the aiming intention graph is 90%, which is higher than the overlap threshold, the corresponding B smart rotation verification code will be retained and sent to the user to be verified as a test verification code for secondary verification. This step is to further confirm the user's identity.

[0114] In practice, various displayed values ​​are obtained from the aiming intention graph. These values ​​represent the trajectory characteristics of the user's operation, such as dragging speed, operation time, or trajectory direction. These characteristics are used to evaluate the degree of match between the user's operation and the expected trajectory. The displayed values ​​are compared with a preset overlap threshold, a pre-set standard used to determine the degree of match between the user's operation trajectory and the expected trajectory. Only Zhixuan verification codes that highly match the expected trajectory are used for secondary verification, which reduces unnecessary verification steps and improves the efficiency of the verification process.

[0115] By comparing the targeting intention graph with the overlap threshold and screening it, we can obtain key data to evaluate the degree of match between user operations and expected trajectories. This helps to filter out possible automated attacks or erroneous operations, further confirm the user's identity, and ensure that only Zhixuan verification codes that highly match the expected trajectory are used for secondary verification, thereby improving the security and efficiency of the verification process, effectively reducing false positives and missed negatives, and improving the overall effectiveness of the Zhixuan verification code system.

[0116] Specifically, the steps for collecting verification time include:

[0117] When the user to be verified receives the test verification code, the timer starts counting;

[0118] When the movable slider is aligned with the notch position, the timer stops and the corresponding verification time is calculated and collected;

[0119] When the movable slider fails the secondary verification, the timer automatically cancels the timing and resets the time to zero.

[0120] In some possible implementations, if the user to be verified receives a test verification code at 12:00, a timer starts counting. If the user to be verified drags the movable slider to align with the notch at 12:01, the timer stops counting, and the verification time is one minute. If the user to be verified receives a test verification code at 12:00 and fails the secondary verification at 12:01, the timer automatically cancels counting and resets to zero.

[0121] In practice, when a user receives a test verification code, the system starts a timer, marking the beginning of the secondary verification process. The timer records the time it takes for the user to complete the verification process from receiving the verification code.

[0122] Specifically, the verification time is compared with a time threshold, where

[0123] The time threshold is the maximum length of time required for the user to be verified to complete the second verification, which is related to the security performance of the Zhixuan verification code.

[0124] In a specific implementation, if the user's verification time is higher than the time threshold, the system will consider that the user's operation speed is too slow, which may indicate that the user is not a human but an automated robot or script. In this case, the system will require the user to re-verify or take other security measures.

[0125] If the user's verification time is within the time threshold, the system considers the user's verification operation to be normal and can continue with the subsequent identity verification process.

[0126] The time threshold setting is not static; it is adjusted based on changes in security requirements, user behavior statistics, and changes in attack patterns. By dynamically adjusting the time threshold, the system can better adapt to different security environments, improving its adaptability and security.

[0127] By comparing verification time with the time threshold, the system can effectively filter out automated attacks, reduce false positives and false negatives, and improve the security of the verification process. At the same time, setting a reasonable time threshold also helps improve the user experience and avoids legitimate users being mistakenly rejected due to overly strict security measures.

[0128] Specifically, when the verification time is lower than the time threshold, the corresponding test verification code is retained and marked as a sample verification code; when the verification time is higher than the time threshold, the corresponding test verification code is cleared and marked as an error verification code.

[0129] This strategy can effectively identify and handle possible security threats while minimizing interference with normal users.

[0130] Retaining sample CAPTCHAs helps the system learn and adapt to new attack patterns, while removing incorrect CAPTCHAs helps reduce wasted system resources. Based on the statistical data and analysis of sample and incorrect CAPTCHAs, the system can dynamically adjust time thresholds and other security parameters to improve adaptability and accuracy. This data can also be used to provide feedback to users, helping them understand potential security risks and best practices. This not only helps improve the security of the Zhixuan CAPTCHA system, but also optimizes system performance and user experience.

[0131] In some possible implementations, if the time threshold is 10s, and the verification time of test verification code A is monitored to be 8s, the verification time is lower than the time threshold, the corresponding test verification code will be retained and marked as a sample verification code; if the verification time of test verification code B is monitored to be 12s, the verification time is higher than the time threshold, the test verification code B will be cleared and marked as an incorrect verification code.

[0132] By calculating and collecting the verification time of the secondary verification and comparing the verification time with the time threshold, retaining sample verification codes and clearing incorrect verification codes, the security and accuracy of Zhixuan verification codes are further enhanced. By setting a reasonable time threshold, the system can more effectively identify and distinguish between real users and robot operations, thereby improving the security of Zhixuan verification codes and ensuring that only real users can pass the verification, while automated attacks are effectively blocked.

[0133] Specifically, obtaining the resolution of the sample verification code, improving the resolution of the sample verification code using image technology, and converting the new resolution into corresponding image quality parameters;

[0134] The resolution includes the height and width of the sample verification code;

[0135] The image processing technique is to divide a single resolution into several sub-resolutions.

[0136] In specific implementations, it is necessary to determine the original resolution of the sample verification code, which includes the height and width of the verification code image. Resolution is a key parameter of image quality, which determines the clarity and level of detail of the image. The resolution of the sample verification code can be improved through image technology. Preferably, image super-resolution technology can be used to improve image resolution through machine learning algorithms. This technology may include using deep learning models, such as convolutional neural networks, to predict and enhance high-resolution details in images.

[0137] Image processing techniques can segment a single resolution into several sub-resolutions. In specific implementations, the GrabCut algorithm can be used to segment an image into different regions or objects. In addition, clustering-based segmentation methods such as K-means and SLIC algorithms can also be used to segment an image into multiple superpixels, which can be regarded as sub-resolution units.

[0138] After increasing resolution, the new resolution can be converted into image quality metrics. These metrics include line pairs per picture height (LP / PH), lines per millimeter (L / mm), cycles per millimeter (Cycles / mm), and line width per picture height (LW / PH). These metrics help assess image clarity and detail.

[0139] Through the above steps, the resolution of the sample verification code can be effectively improved, and its new resolution can be converted into corresponding image quality parameters, thereby providing higher quality image data for subsequent image processing and recognition tasks.

[0140] Specifically, the image quality parameters are saved and marked as sample parameters, and the setter performs batch image processing and applies the sample parameters to all Zhixuan verification codes;

[0141] Among them, batch image processing is that the setter traverses all the smart rotation verification codes and updates the corresponding image quality parameters to sample parameters.

[0142] In specific implementations, after increasing the resolution of the sample CAPTCHA and converting it into image quality parameters, these parameters are saved. These parameters, including LP / PH, L / mm, Cycles / mm, and LW / PH, represent image clarity and detail. The saved image quality parameters are labeled "sample parameters." These sample parameters serve as a benchmark or reference for quality control during subsequent batch processing of Zhixuan CAPTCHA images.

[0143] Use a configurator (which could be a software tool or script to automate the process) to iterate over all the Zhixuan CAPTCHA images. This configurator will be responsible for applying the sample parameters to each CAPTCHA image.

[0144] During batch processing, the setter updates the corresponding image quality parameters to the sample parameters. This means that each Zhixuan CAPTCHA image will be adjusted according to the sample parameters to ensure that they all meet the same quality standards.

[0145] The specific operation of applying sample parameters includes adjusting the resolution, contrast, brightness, etc. of the image to ensure the clarity and recognizability of the image. This can be achieved through various image processing techniques such as interpolation and image enhancement.

[0146] After batch processing, Zhixuan CAPTCHA images are verified to ensure they meet expected image quality standards. If any issues are found, they are fed back into the processing pipeline, sample parameters are adjusted, and batch processing is repeated. This ensures that all Zhixuan CAPTCHA images have consistent and high image quality, which helps improve CAPTCHA recognition rates and user experience. This also helps reduce security risks caused by image quality issues, such as automated attacks.

[0147] By optimizing the image quality of the sample verification code and applying it as a standard parameter to all Zhixuan verification codes, the consistency and high quality of Zhixuan verification codes can be ensured. This not only helps to improve the security of Zhixuan verification codes and reduce the misrecognition rate, but also enhances the user's visual experience, making Zhixuan verification codes easier to be correctly identified and processed. Through batch processing, time and resources can be saved and the efficiency of the Zhixuan verification code management system can be improved.

[0148] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0149] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for accurately identifying the characteristics of smart verification codes and their contents, characterized in that: include: Step S1, screening the Zhixuan verification codes that have not passed the verification, and collecting the aiming trajectory images of the Zhixuan verification codes, wherein, The smart spin verification code includes a fixed background and a movable slider, and the fixed background is provided with a notch position; When the movable slider is aligned with the notch, it is determined that the smart rotation verification code has passed the verification; The aiming trajectory image is an image of a trajectory of the movable slider being dragged from an initial position to a final position by the user to be verified; Step S2, determining the trajectory features of the aiming trajectory image, drawing an aiming intention map, and annotating the aiming intention map according to the trajectory features; Step S3: compare the aiming intention graph with the overlap threshold and filter it, retain the smart spin verification code that exceeds the overlap threshold, and perform secondary verification as the test verification code, and collect the corresponding verification time, wherein, The overlap threshold is the minimum standard for the aiming trajectory image to overlap with the correct trajectory; Step S4: Compare the verification time with a time threshold, retain the test verification code below the time threshold and use it as a sample verification code, wherein: The verification time is the time required for the user to be verified to complete the dragging process and pass the secondary verification; Step S5: extract the image quality parameters of the sample verification code and apply them as sample parameters to all smart verification codes, wherein: The image quality parameter is the resolution corresponding to the sample verification code; The steps of comparing the aiming intention map with the coincidence threshold and screening include: Obtaining each displayed value in the aiming intention graph; The displayed value is compared with the overlap threshold, wherein, When the displayed value is lower than the overlap threshold, the corresponding smart rotation verification code is cleared; When the displayed value is higher than the overlap threshold, the corresponding Zhixuan verification code is retained and sent to the user to be verified as a test verification code for the secondary verification.

2. The method for accurately identifying the characteristics of verification codes and contents according to claim 1 is characterized in that: The steps for collecting the aiming trajectory image of the Zhixuan verification code include: determining an initial position and a final position of the movable slider; Determine whether the final position is a gap position, and filter the correct trajectory; Drawing the aiming trajectory image according to the dragging record of the movable slider; The correct trajectory is the movement trajectory of the movable slider when the user to be verified drags the movable slider from the initial position to the gap position.

3. The method for accurately identifying the characteristics of verification codes and contents according to claim 2 is characterized in that: The steps to creating a targeting intent map include: Preprocessing the aiming trajectory image to generate corresponding trajectory preprocessing data; The intention learning model learns the trajectory preprocessing data and generates a corresponding aiming intention graph; marking the aiming intention graph according to the trajectory characteristics; The intention learning model is generated by training a trajectory training set formed by trajectory preprocessing data corresponding to the aiming trajectory image.

4. The method for accurately identifying the characteristics of verification codes and contents according to claim 3 is characterized in that: The steps of preprocessing the aiming trajectory image include: The trajectory feature is determined according to the aiming trajectory image, wherein: The trajectory characteristics include the dragging speed, operation time and / or trajectory direction corresponding to the aiming trajectory image; Perform image filtering on the aiming trajectory image to obtain corresponding trajectory filtering data, wherein: The image filtering is based on the trajectory characteristics, and removes the aiming trajectory images that do not meet the trajectory characteristics; The trajectory filtering data is segmented according to a standard learning rate to form corresponding trajectory preprocessing data, wherein the standard learning rate is a learning rate that can be recognized by the intentional learning model, and for single learning, the corresponding standard learning rate is a single learning rate.

5. The method for accurately identifying the characteristics of verification codes and contents according to claim 4 is characterized in that: The steps for collecting verification time include: When the user to be verified receives the test verification code, the timer starts timing; When the movable slider is aligned with the notch, the timer stops timing and calculates and collects the corresponding verification time; Wherein, when the movable slider fails the secondary verification, the timer automatically cancels the timing and resets the time to zero.

6. The method for accurately identifying the characteristics of smart verification codes and their contents according to claim 5 is characterized in that: The verification time is compared with the time threshold, wherein, The time threshold is the maximum time required for the user to be verified to complete the secondary verification, which is related to the security performance of the Zhixuan verification code.

7. The method for accurately identifying the characteristics of verification codes and contents according to claim 6 is characterized in that: When the verification time is lower than the time threshold, the corresponding test verification code is retained and marked as a sample verification code. When the verification time is higher than the time threshold, the corresponding test verification code is cleared and marked as an error verification code.

8. The method for accurately identifying the characteristics of verification codes and contents according to claim 7 is characterized in that: Obtaining the resolution of the sample verification code, improving the resolution of the sample verification code using image technology, and converting the new resolution into a corresponding image quality parameter; Wherein, the resolution includes the height and width of the sample verification code; The image technique is to divide a single resolution into several sub-resolutions.

9. The method for accurately identifying the characteristics of verification codes and contents according to claim 8 is characterized in that: The image quality parameters are saved and marked as sample parameters, and the setter performs batch image processing and applies the sample parameters to all smart spin verification codes; The batch image processing is that the setter traverses all the smart rotation verification codes and updates the corresponding image quality parameters to the sample parameters.

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