Electronic signature verification method, device, equipment, storage medium and computer program product
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA MOBILE FINANCIAL TECHNOLOGY CO LTD
- Filing Date
- 2025-02-05
- Publication Date
- 2026-08-07
AI Technical Summary
[0003]本申请的主要目的在于提供一种电子签名验证方法、装置、设备、存储介质及计算机程序产品，旨在解决现有的电子签名验证技术易被模仿或复制，缺乏有效的防伪机制，存在安全性较低的缺陷的技术问题
[0031]在本申请中，公开了采集用户在电子签名过程中各个签名笔顺的力度特征，根据各个签名笔顺的力度特征和各个标准签名笔顺的标准力度特征确定各个签名笔顺与各个标准签名笔顺之间的力度相似度，基于力度相似度计算电子签名的总体相似度，并根据总体相似度对电子签名进行验证；由于本申请通过对签名笔顺及力度特征进行分析来验证电子签名，从而减少了电子签名被破解的风险，提高了电子签名的防伪能力，进而提高了电子签名的安全性。
Smart Images
Figure CN122528124A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of network security technology, and in particular to an electronic signature verification method, apparatus, device, storage medium, and computer program product. Background Technology
[0002] Currently, electronic signature verification technology mainly relies on digital certificates and cryptographic algorithms. For example, a hash function is used to calculate the hash value of the original data, the certificate holder's private key is used to encrypt the hash value to generate a digital signature, and the digital signature is appended to the original data. During signature verification, the public key is extracted from the certificate holder's digital certificate, and the public key is used to decrypt the digital signature to obtain another hash value. Verification is achieved by comparing whether the two hash values match. However, existing electronic signature verification technologies are easily imitated or copied, lack effective anti-counterfeiting mechanisms, and have relatively low security. Summary of the Invention
[0003] The main purpose of this application is to provide an electronic signature verification method, apparatus, device, storage medium, and computer program product, which aims to solve the technical problems of existing electronic signature verification technology being easily imitated or copied, lacking an effective anti-counterfeiting mechanism, and having low security.
[0004] To achieve the above objectives, this application provides an electronic signature verification method, the electronic signature verification method comprising:
[0005] Collect the strength characteristics of each stroke of the user's signature during the electronic signature process;
[0006] The similarity of strength between each signature stroke order and each standard signature stroke order is determined based on the strength characteristics of each signature stroke order and the standard strength characteristics of each standard signature stroke order.
[0007] The overall similarity of the electronic signature is calculated based on the strength similarity, and the electronic signature is verified based on the overall similarity.
[0008] Optionally, the force characteristics include at least one of the following: maximum pressure value of the signature stroke, coordinates of the maximum pressure value point of the signature stroke, minimum pressure value of the signature stroke, coordinates of the minimum pressure value point of the signature stroke, average force value of the signature stroke, force variation trend of the signature stroke, duration of force application of the signature stroke, frequency of force application of the signature stroke, and direction of force application.
[0009] Optionally, the collection of the force characteristics of each signature stroke order during the electronic signature process includes:
[0010] Collect the original force data of each stroke of the signature during the electronic signature process;
[0011] The raw force data is preprocessed to obtain preprocessed data;
[0012] Extract the strength features of each signature stroke order from the preprocessed data.
[0013] Optionally, determining the similarity in strength between each signature stroke order and each standard signature stroke order based on the strength characteristics of each signature stroke order and the standard strength characteristics of each standard signature stroke order includes:
[0014] Based on the strength characteristics of each signature stroke order, feature vectors for each signature stroke order are constructed, and based on the standard strength characteristics of each standard signature stroke order, feature vectors for each standard signature stroke order are constructed.
[0015] Based on the feature vectors of each signature stroke order and each standard signature stroke order, the strength similarity between each signature stroke order and each standard signature stroke order is calculated using a preset strength similarity model.
[0016] Optionally, before determining the strength similarity between each signature stroke order and each standard signature stroke order based on the strength characteristics of each signature stroke order and the standard strength characteristics of each standard signature stroke order, the method further includes:
[0017] Obtain the user information of the user;
[0018] Match the standard signature stroke order corresponding to the user information in the preset signature library;
[0019] Search the preset force pattern database for the standard force feature corresponding to the standard signature stroke order.
[0020] Optionally, before collecting the strength characteristics of each signature stroke order during the electronic signature process, the method further includes:
[0021] Collect electronic signature samples from the users and construct a training dataset based on the electronic signature samples;
[0022] The training dataset is learned by a machine learning model to construct a preset signature library and a preset strength pattern database.
[0023] Furthermore, to achieve the above objectives, this application also proposes an electronic signature verification device, which includes:
[0024] The feature acquisition module is used to collect the strength characteristics of each stroke of the user's signature during the electronic signature process.
[0025] The feature calculation module is used to determine the strength similarity between each signature stroke order and each standard signature stroke order based on the strength characteristics of each signature stroke order and the standard strength characteristics of each standard signature stroke order.
[0026] The signature verification module is used to calculate the overall similarity of the electronic signature based on the strength similarity, and to verify the electronic signature according to the overall similarity.
[0027] In addition, to achieve the above objectives, this application also proposes an electronic signature verification device, which includes a memory, a processor, and an electronic signature verification program stored in the memory and executable on the processor, the electronic signature verification program being configured to implement the electronic signature verification method as described above.
[0028] In addition, to achieve the above objectives, this application also proposes a storage medium storing an electronic signature verification program, which, when executed by a processor, implements the electronic signature verification method as described above.
[0029] In addition, to achieve the above objectives, this application also provides a computer program product, which includes an electronic signature verification program, and the electronic signature verification program implements the electronic signature verification method as described above when executed by a processor.
[0030] One or more technical solutions proposed in this application have at least the following technical effects:
[0031] This application discloses the method of collecting the strength characteristics of each signature stroke order during the electronic signature process, determining the strength similarity between each signature stroke order and each standard signature stroke order based on the strength characteristics of each signature stroke order and the standard strength characteristics of each standard signature stroke order, calculating the overall similarity of the electronic signature based on the strength similarity, and verifying the electronic signature based on the overall similarity. Since this application verifies the electronic signature by analyzing the signature stroke order and strength characteristics, it reduces the risk of the electronic signature being cracked, improves the anti-counterfeiting capability of the electronic signature, and thus improves the security of the electronic signature. Attached Figure Description
[0032] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0033] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0034] Figure 1 This is a flowchart illustrating the first embodiment of the electronic signature verification method of this application;
[0035] Figure 2 This is a flowchart illustrating the second embodiment of the electronic signature verification method of this application;
[0036] Figure 3 This is a flowchart illustrating the third embodiment of the electronic signature verification method of this application;
[0037] Figure 4 This is a schematic diagram of the module structure of the electronic signature verification device according to an embodiment of this application;
[0038] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the electronic signature verification method in this application embodiment.
[0039] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0040] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.
[0041] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.
[0042] Currently, electronic signature verification technology mainly relies on digital certificates and cryptographic algorithms, with the specific methods as follows:
[0043] 1. A digital certificate is required. A digital certificate is a file used to verify a digital identity. It contains the user's public key and other information signed by a trusted third party (usually a certificate authority). Digital certificates typically contain the following information: holder information, public key, validity period, issuer information, and digital signature.
[0044] 2. It requires reliance on cryptographic algorithms. The main cryptographic algorithms used for electronic signature verification include, but are not limited to:
[0045] Asymmetric encryption algorithms: These algorithms use a pair of keys—a public key and a private key. The public key is used to encrypt data, while the private key is used to decrypt it; in signature scenarios, the private key is used to generate the signature, and the public key is used to verify the signature.
[0046] Hash functions: Hash functions convert data of arbitrary length into a fixed-length hash value, often used to ensure data integrity. In the signing process, the original data is first hashed, and then the hash value is encrypted using a private key to generate a signature.
[0047] 3. Electronic signature generation and verification process:
[0048] (1) Signature generation: The signature process usually involves the following steps: using a hash function to calculate the hash value of the original data; using the holder's private key to encrypt the hash value and generate a digital signature; and attaching the digital signature to the original data.
[0049] (2) Signature Verification: The signature verification process is as follows: Obtain the certificate holder's digital certificate and extract the public key from it; recalculate the hash value of the original data using the same hash function; decrypt the digital signature using the certificate holder's public key to obtain another hash value; compare whether the two hash values are consistent. If they are consistent, the signature is valid; if they are inconsistent, the signature is invalid.
[0050] While the above methods ensure the security of electronic signatures to a certain extent, they have limitations, which are as follows:
[0051] 1. Vulnerable to counterfeiting: Electronic signature verification technology that relies solely on image matching is easily imitated or copied, lacking an effective anti-counterfeiting mechanism.
[0052] 2. Lack of personalization: Most electronic signature systems do not make full use of the uniqueness of personal signatures, such as the speed, direction, and pressure of signing.
[0053] 3. Insufficient security: With the improvement of computing power, the risk of cracking encryption algorithms increases, resulting in a decrease in the security of electronic signatures.
[0054] 4. Poor user experience: Existing electronic signature systems often require users to remember complex passwords or use additional authentication tools, increasing the burden on users.
[0055] Therefore, in order to overcome the above defects, this application provides a solution, which includes: collecting the strength characteristics of each signature stroke order during the electronic signature process, determining the strength similarity between each signature stroke order and each standard signature stroke order based on the strength characteristics of each signature stroke order and the standard strength characteristics of each standard signature stroke order, calculating the overall similarity of the electronic signature based on the strength similarity, and verifying the electronic signature based on the overall similarity; since this application verifies the electronic signature by analyzing the signature stroke order and strength characteristics, the following effects are achieved: (1) Improved anti-counterfeiting capability: Through the analysis of signature stroke order and strength characteristics, it is possible to effectively distinguish between legitimate signatures and forged signatures. (2) Enhanced personalized verification: Considering the differences in signature stroke order and strength of each person, the accuracy of verification is improved. (3) Improved security: The security of the electronic signature system is improved by using signature stroke order and strength recognition technology, reducing the risk of being cracked. (4) Improved user experience: The identity verification process is simplified, so that users do not need to remember complex passwords or use additional identity verification tools to complete the signature verification.
[0056] It should be noted that the executing entity in this embodiment may be an electronic signature verification device with data processing, network communication and program running functions, such as an electronic device, or other electronic devices that can achieve the same or similar functions. This embodiment does not limit this.
[0057] Based on this, the embodiments of this application provide an electronic signature verification method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the electronic signature verification method of this application.
[0058] In the first embodiment, the electronic signature verification method includes:
[0059] Step S10: Collect the strength characteristics of each stroke of the user's signature during the electronic signature process.
[0060] It should be understood that, to facilitate subsequent calculation of the similarity in pressure between each signature stroke order and each standard signature stroke order, this embodiment first collects the pressure characteristics of each signature stroke order during the electronic signature process, and then performs subsequent processing. Here, an electronic signature can refer to a signature formed by a user through handwriting on an electronic device (such as a touchscreen, tablet, etc.), and can be used for identity verification or document signing. A signature stroke order can refer to the path of strokes written by the user in a certain order during the electronic signature process, with each stroke order representing a part of the signature. Pressure characteristics can refer to the magnitude and variation of pressure applied by the user to the electronic device during the signing process; these characteristics reflect the user's writing habits and pressure patterns.
[0061] In this implementation, to accurately obtain the pressure characteristics of the user's signature, providing foundational data for subsequent feature matching and similarity calculation, this embodiment uses devices such as pressure sensors to collect the stroke order and pressure characteristics of each signature stroke when the user makes an electronic signature on the electronic device. For example, using a high-precision pressure sensor or the pressure sensing technology built into the touchscreen, the signature stroke order and corresponding pressure changes of the user when signing on the electronic device can be recorded in real time. The sensor records information such as the pressure distribution, peak pressure, duration, and trajectory length of each signature stroke during the user's signature process, and converts this information into digital signals to obtain the pressure characteristics of each signature stroke during the electronic signature process.
[0062] Furthermore, to improve the reliability of the force characteristics, the force characteristics include at least one of the following: maximum pressure value of the signature stroke order, coordinates of the maximum pressure value recording point of the signature stroke order, minimum pressure value of the signature stroke order, coordinates of the minimum pressure value recording point of the signature stroke order, average force value of the signature stroke order, force variation trend of the signature stroke order, duration of force application of the signature stroke order, frequency of force application of the signature stroke order, and direction of force application. Wherein, maximum pressure value can refer to the maximum pressure value applied in the signature stroke order. Coordinates of the maximum pressure value recording point can refer to the coordinate position when the pressure reaches its maximum value. Minimum pressure value can refer to the minimum pressure value applied in the signature stroke order. Coordinates of the minimum pressure value recording point can refer to the coordinate position when the pressure reaches its minimum value. Average force value can refer to the average value of the pressure applied in the signature stroke order. Force variation trend can refer to the change of pressure in the signature stroke order over time. Duration of force application can refer to the duration of the signature stroke order at a specific pressure level. Frequency of force application can refer to the number of pressure changes in the signature stroke order. Direction of force application can refer to the direction of force application changes in the signature stroke order.
[0063] For ease of understanding, the following examples are provided, but are not intended to limit this application. As an example, strength features include, but are not limited to:
[0064] Maximum and minimum pressure values: The pressure values applied to each stroke of the signature process. Each stroke is recorded with a corresponding maximum and minimum value. The first point of each stroke is recorded with coordinates O(0,0). The maximum pressure value Fmax in this stroke is recorded with coordinates (A,B), and the minimum pressure value Fmin is recorded with coordinates (M,J).
[0065] Average pressure value: The average pressure applied by each stroke in the entire signing process. Each stroke is recorded with a corresponding average pressure value. The average pressure value of each signature stroke is recorded as P (the average pressure values of all strokes are recorded as p1, p2, p3...pk, where k is the number of signature strokes).
[0066] Intensity variation trend: The trend of intensity variation of each stroke order in the signature process over time. Each stroke order is recorded with a corresponding intensity variation trend, resulting in a set of curves showing the intensity variation of signature stroke order over time. The points on the curve are denoted as TV(t, v), where t is the time point and v is the intensity value.
[0067] Duration of force application: The duration of force application at each stroke order. Each stroke order is recorded with a corresponding duration of force application, denoted as Q. (The average duration of force application for all stroke orders is Q1, Q2, Q3...Qk, where k is the number of stroke orders).
[0068] Force application frequency: The number of times the force changes for each stroke of the signature. Each stroke is recorded with a corresponding force application frequency, denoted as C (the force application frequencies corresponding to all signature strokes are C1, C2, C3...Ck, where k is the number of signature strokes).
[0069] Force direction: The direction of force applied during the signature process changes for each stroke. Each stroke is recorded as a corresponding force direction change, denoted as D (the force frequencies corresponding to all signature strokes are D1, D2, D3...Dk, where k is the number of signature strokes).
[0070] Step S20: Determine the similarity in strength between each signature stroke order and each standard signature stroke order based on the strength characteristics of each signature stroke order and the standard strength characteristics of each standard signature stroke order.
[0071] Understandably, this is done to quantify the similarity between user signatures and standard signatures in terms of strength features, providing a basis for subsequent overall similarity calculations and signature verification. In this embodiment, the strength similarity between each signature stroke order is determined based on the strength features of each signature stroke order and the standard strength features of each standard signature stroke order. Here, a standard signature stroke order can refer to electronic signature stroke orders that are pre-collected and stored in a database as a reference or verification benchmark. A standard strength feature can refer to the strength feature corresponding to a standard signature stroke order. Strength similarity can be an indicator used to measure the similarity between the strength features of a user signature stroke order and the standard strength features of a standard signature stroke order.
[0072] In the specific implementation, a specific distance metric or similarity function (such as Euclidean distance, cosine similarity, etc.) is used to calculate the similarity between the strength features of the user's signature stroke order and the strength features of the standard signature stroke order, thereby obtaining the strength similarity between each signature stroke order and each standard signature stroke order.
[0073] Furthermore, to improve the efficiency of electronic signature verification, step S20 includes: constructing feature vectors for each signature stroke order based on the force characteristics of each stroke order, and constructing feature vectors for each standard signature stroke order based on the standard force characteristics of each standard signature stroke order; calculating the force similarity between each signature stroke order and each standard signature stroke order using a preset force similarity model based on the feature vectors of each signature stroke order and the feature vectors of each standard signature stroke order. Here, the feature vector can refer to the representation of force characteristics (such as the maximum pressure of the signature stroke order, the coordinates of the maximum pressure point, the minimum pressure of the signature stroke order, the coordinates of the minimum pressure point, the average force value of the signature stroke order, the force change trend of the signature stroke order, the duration of force application, the frequency of force application, and the direction of force application, etc.) in the form of a mathematical vector, used for subsequent similarity calculation. The force similarity model can refer to an algorithm or model used to calculate the degree of similarity between the feature vectors of each signature stroke order and the feature vectors of each standard signature stroke order.
[0074] For ease of understanding, the following example is provided, but it does not limit this application. As an example, suppose that after the above feature extraction, each signature stroke order will yield the following set of values, one of which is 9 values:
[0075] 1) The maximum pressure value Fmax in this stroke order;
[0076] 2) Record the coordinates (A, B) of the point where the pressure is at its maximum in this stroke order;
[0077] 3) The minimum pressure value Fmin for this stroke order;
[0078] 4) Record the coordinates (M, J) of the point where the pressure is minimum for this stroke order;
[0079] 5) Average force value P;
[0080] 6) Trend of intensity change TV(t, v);
[0081] 7) Duration of force application Q;
[0082] 8) Force application frequency C;
[0083] 9) Direction of force application: D.
[0084] The above 9 values correspond to the dimensions in the vector of each signature stroke order, that is, each signature stroke order vector corresponds to 9 dimensions.
[0085] Each signature stroke order has a corresponding vector X = (Fmax, (A, B), Fmin, (M, J), P, TV(t, v), S, C, D).
[0086] Similarly, the standard signature stroke order also has a corresponding vector Y, which also includes the above 9 dimensions. Next, the similarity of each signature stroke order is compared using the cosine similarity algorithm. Then, all the obtained similarity values are added together and averaged (the sum of all stroke order similarities is divided by the number of strokes k) to obtain the overall similarity. The final overall similarity value is compared with 1. The closer it is to 1, the more similar it is.
[0087] The formula for calculating the strength similarity between each signature stroke order and each standard signature stroke order using the cosine similarity algorithm is as follows:
[0088]
[0089] In the formula, Cosine similarity(X,Y) represents the cosine similarity between vectors X and Y, where x i Let y represent the i-th feature in vector X. i Let represent the i-th feature in vector Y, and n represent the total number of dimensions of the vector.
[0090] Meaning: Cosine similarity measures the cosine of the angle between two vectors, ranging from -1 to 1. When two vectors are identical (0°), the similarity is 1; when two vectors are opposite (180°), the similarity is -1. Cosine similarity does not consider the magnitude of the vectors, only their direction, making it suitable for comparing the directional similarity of vectors. This algorithm is used to calculate the cosine similarity between the starting position and the maximum and minimum values of each signature stroke order and the starting position and maximum and minimum values of the standard force pattern.
[0091] Step S30: Calculate the overall similarity of the electronic signature based on the strength similarity, and verify the electronic signature according to the overall similarity.
[0092] It should be understood that, in order to comprehensively evaluate the overall similarity between a user's signature and a standard signature, and to achieve effective verification of the electronic signature, this embodiment calculates the overall similarity of the electronic signature based on strength similarity, and verifies the electronic signature according to the overall similarity. Here, overall similarity can refer to the degree of similarity between the entire electronic signature and the standard electronic signature after comprehensively considering the strength similarity of all signature strokes.
[0093] In practice, the similarity scores of all signature strokes are aggregated or weighted to obtain the overall similarity score of the entire electronic signature. Based on a preset similarity threshold (e.g., 0.95), the authenticity or validity of the electronic signature is determined. For example, if the overall similarity score of the electronic signature is greater than or equal to the preset similarity threshold, the electronic signature is considered verified; if the overall similarity score is less than the preset similarity threshold, the electronic signature is considered unverified.
[0094] For ease of understanding, examples are provided below, but these are not intended to limit this application. As an example, a comparison of the similarity of the stroke order of various signatures is presented below:
[0095] Signature stroke order 1:
[0096] The similarity value S1 is obtained by using the cosine similarity algorithm to compare the vector X corresponding to the stroke order 1 of the newly input signature with the vector Y corresponding to the stroke order 1 of the standard signature.
[0097] Signature stroke order 2:
[0098] The similarity value S2 is obtained by using the cosine similarity algorithm to compare the vector X corresponding to the stroke order 2 of the newly input signature with the vector Y corresponding to the stroke order 2 of the standard signature.
[0099] And so on, the stroke order for the signature is k:
[0100] The similarity value Sk is obtained by comparing the vector X corresponding to the stroke order k of the newly input signature with the vector Y corresponding to the stroke order k of the standard signature using the cosine similarity algorithm.
[0101] If the similarity value obtained in any stroke order of the signature deviates significantly (for example, the deviation from 1 is greater than 0.08, and this 0.08 can be set according to the actual situation), then the signatures are considered dissimilar.
[0102] The formula for calculating the overall similarity of electronic signatures based on strength similarity is as follows:
[0103] The overall similarity of electronic signatures = (S1 + S2 + ... + Sk) / k;
[0104] Where S1+S2+...+Sk represents the similarity of each signature stroke order, and k is the number of signature stroke orders. The higher the authenticity of the signature, the closer the overall similarity of the electronic signature is to 1, and the more similar the signatures are (for example, it can be stipulated that the overall similarity of the signatures is between [0.95, 1.00], which means that the deviation does not exceed 0.05, and the signatures are dissimilar if the deviation exceeds 0.05. This 0.05 can be set according to the actual situation).
[0105] This embodiment verifies electronic signatures by analyzing the stroke order and pressure characteristics of the signature, thereby reducing the risk of electronic signatures being cracked, improving the anti-counterfeiting capability of electronic signatures, and thus improving the security of electronic signatures.
[0106] Reference Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the electronic signature verification method of this application, based on the above. Figure 1 The first embodiment shown illustrates a second embodiment of the electronic signature verification method of this application.
[0107] In the second embodiment, step S10 includes:
[0108] Step S101: Collect the original force data of each stroke order of the user's signature during the electronic signature process.
[0109] It should be understood that, in order to improve the accuracy of the force characteristics, in this embodiment, the collected raw force data is preprocessed to obtain preprocessed data, and the force characteristics of each signature stroke are extracted from the preprocessed data. The raw force data can refer to the force change data of each signature stroke recorded in real time by the user during the signing process using an electronic device (such as a pressure sensor or touchscreen).
[0110] In practice, high-precision pressure sensors or pressure sensing technology built into the touchscreen are used to record in real time the stroke order and corresponding pressure changes when a user signs their name on the electronic device. The sensor converts the pressure changes during the user's signature process into digital signals, forming raw pressure data.
[0111] Step S102: Preprocess the original force data to obtain preprocessed data.
[0112] Understandably, to improve the accuracy and consistency of the data and provide a high-quality data foundation for subsequent feature extraction and similarity calculation, this embodiment also preprocesses the original force data to obtain preprocessed data. Preprocessing refers to a series of operations performed on the original force data, aimed at removing noise, smoothing the data, standardizing, etc., to improve data quality and the accuracy of subsequent analysis.
[0113] In practical implementation, the collected raw force data can be denoised to remove outliers caused by equipment errors or environmental factors. Data smoothing can also be performed to reduce data fluctuations and improve data quality. Furthermore, the collected raw force data can be standardized or normalized to ensure the comparability of force data between different signatures.
[0114] Step S103: Extract the strength features of each signature stroke order from the preprocessed data.
[0115] It should be understood that key force features are extracted from the preprocessed data, such as the maximum pressure value of the signature stroke, the coordinates of the record point of the maximum pressure value of the signature stroke, the minimum pressure value of the signature stroke, the coordinates of the record point of the minimum pressure value of the signature stroke, the average force value of the signature stroke, the force variation trend of the signature stroke, the duration of force application of the signature stroke, the frequency of force application of the signature stroke, and the direction of force application. These features can be obtained by calculation, statistics, or analysis of the preprocessed data points.
[0116] This embodiment preprocesses the collected raw force data to obtain preprocessed data, and extracts the force features of each signature stroke order from the preprocessed data, thereby improving the accuracy of the force features.
[0117] Reference Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the electronic signature verification method of this application. Based on the above embodiments, the third embodiment of the electronic signature verification method of this application is proposed.
[0118] In the third embodiment, before step S20, the method further includes:
[0119] Step S11: Obtain the user information of the user.
[0120] It should be understood that, in order to provide a benchmark for subsequent comparison with the strength characteristics of the user's signature stroke order, this embodiment first matches the standard signature stroke order corresponding to the user information in a preset signature library, and then searches for the standard strength characteristics corresponding to the standard signature stroke order in a preset strength pattern database. Here, user information can refer to information used to identify the user, such as username, ID, password, etc., or biometric information (such as fingerprints, facial recognition, etc.). In specific implementations, the user's identity information is obtained through input, scanning, or biometric recognition before or during the user's electronic signature process.
[0121] Step S12: Match the standard signature stroke order corresponding to the user information in the preset signature library.
[0122] It is understood that the preset signature library can refer to a database storing the standard signatures of multiple users. Each user's standard signature may include their signature stroke order and / or signature image, etc., and this embodiment does not impose any limitations on this. In specific implementation, based on the obtained user information, the preset signature library is searched to find the standard signature stroke order corresponding to that user information.
[0123] Step S13: Search for the standard force feature corresponding to the standard signature stroke order in the preset force mode database.
[0124] It should be understood that the preset force pattern database can refer to a database storing standard force characteristics corresponding to the standard signature stroke order. These force characteristics may include the maximum pressure of the signature stroke order, the coordinates of the record point of the maximum pressure of the signature stroke order, the minimum pressure of the signature stroke order, the coordinates of the record point of the minimum pressure of the signature stroke order, the average force value of the signature stroke order, the force change trend of the signature stroke order, the duration of force application of the signature stroke order, the frequency of force application of the signature stroke order, and the direction of force application, etc. This embodiment does not limit these aspects. In specific implementation, based on the found standard signature stroke order, the standard force characteristic corresponding to that stroke order is searched in the preset force pattern database.
[0125] Furthermore, to construct a preset signature library and a preset strength pattern database containing standard signature stroke order and strength features, providing a benchmark for subsequent user signature verification, before step S10, the method further includes: collecting electronic signature samples from the user and constructing a training dataset based on the electronic signature samples; and using a machine learning model to learn from the training dataset to construct the preset signature library and the preset strength pattern database. Here, the electronic signature sample can refer to signature data generated by the user on an electronic device through handwriting, containing information such as signature stroke order and strength variations. The training dataset can refer to a collection of multiple electronic signature samples used for training and learning the machine learning model. The machine learning model can refer to an algorithm or system capable of automatically identifying and predicting new data by learning from a large amount of data.
[0126] In the implementation, signature samples from multiple users are collected, and the stroke order and strength characteristics of each sample are recorded. Machine learning algorithms (such as support vector machines and neural networks) are used to train the training dataset, constructing a preset signature library and a preset strength pattern database. During training, algorithm parameters can be adjusted through methods such as cross-validation to improve the model's generalization ability and accuracy.
[0127] This embodiment first matches the standard signature stroke order corresponding to the user information in the preset signature library, and then searches for the standard strength feature corresponding to the standard signature stroke order in the preset strength mode database. This provides a benchmark for subsequent comparison with the strength feature of the user's signature stroke order, thereby improving the accuracy of electronic signature verification.
[0128] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the electronic signature verification method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.
[0129] This application also provides an electronic signature verification device, please refer to... Figure 4 The electronic signature verification device includes:
[0130] Feature acquisition module 10 is used to collect the strength features of each stroke of the signature during the electronic signature process;
[0131] The feature calculation module 20 is used to determine the strength similarity between each signature stroke order and each standard signature stroke order based on the strength characteristics of each signature stroke order and the standard strength characteristics of each standard signature stroke order.
[0132] The signature verification module 30 is used to calculate the overall similarity of the electronic signature based on the strength similarity, and to verify the electronic signature according to the overall similarity.
[0133] The electronic signature verification device provided in this application, employing the electronic signature verification method in the above embodiments, can solve the technical problems of existing electronic signature verification technologies being easily imitated or copied, lacking effective anti-counterfeiting mechanisms, and exhibiting low security. Compared with the prior art, the beneficial effects of the electronic signature verification device provided in this application are the same as those of the electronic signature verification method provided in the above embodiments, and other technical features in the electronic signature verification device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.
[0134] This application provides an electronic signature verification device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the electronic signature verification method in Embodiment 1 above.
[0135] The following is for reference. Figure 5 This document illustrates a structural diagram of an electronic signature verification device suitable for implementing embodiments of this application. The electronic signature verification device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The electronic signature verification device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.
[0136] like Figure 5 As shown, the electronic signature verification device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in ROM (Read Only Memory) 1002 or a program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the electronic signature verification device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touch screens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, LCDs (Liquid Crystal Displays), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the electronic signature verification device to communicate wirelessly or wiredly with other devices to exchange data. Although the figures show electronic signature verification devices with various systems, it should be understood that implementing or having all of the systems shown is not required. More or fewer systems may be implemented alternatively.
[0137] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.
[0138] The electronic signature verification device provided in this application, employing the electronic signature verification method described in the above embodiments, can solve the technical problems of existing electronic signature verification technologies being easily imitated or copied, lacking effective anti-counterfeiting mechanisms, and exhibiting low security. Compared with the prior art, the beneficial effects of the electronic signature verification device provided in this application are the same as those of the electronic signature verification method provided in the above embodiments, and other technical features in this electronic signature verification device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.
[0139] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.
[0140] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
[0141] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the electronic signature verification method in the above embodiments.
[0142] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory), or flash memory, optical fiber, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.
[0143] The aforementioned computer-readable storage medium may be included in the electronic signature verification device; or it may exist independently and not be assembled into the electronic signature verification device.
[0144] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic signature verification device, cause the electronic signature verification device to perform the aforementioned electronic signature verification method.
[0145] Computer program code for performing the operations of this application can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, and C++, as well as conventional procedural programming languages such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0146] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0147] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.
[0148] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described electronic signature verification method. This addresses the technical problems of existing electronic signature verification technologies being easily imitated or copied, lacking effective anti-counterfeiting mechanisms, and exhibiting low security. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the electronic signature verification method provided in the above embodiments, and will not be elaborated upon here.
[0149] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the electronic signature verification method as described above.
[0150] The computer program product provided in this application can solve the technical problems of existing electronic signature verification technologies being easily imitated or copied, lacking effective anti-counterfeiting mechanisms, and having low security. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the electronic signature verification method provided in the above embodiments, and will not be repeated here.
[0151] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.
Claims
1. An electronic signature verification method, characterized in that, The electronic signature verification method includes: Collect the strength characteristics of each stroke of the user's signature during the electronic signature process; The similarity of strength between each signature stroke order and each standard signature stroke order is determined based on the strength characteristics of each signature stroke order and the standard strength characteristics of each standard signature stroke order. The overall similarity of the electronic signature is calculated based on the strength similarity, and the electronic signature is verified based on the overall similarity.
2. The electronic signature verification method as described in claim 1, characterized in that, The force characteristics include at least one of the following: maximum pressure value of the signature stroke order, coordinates of the maximum pressure value point of the signature stroke order, minimum pressure value of the signature stroke order, coordinates of the minimum pressure value point of the signature stroke order, average force value of the signature stroke order, force variation trend of the signature stroke order, duration of force application of the signature stroke order, frequency of force application of the signature stroke order, and direction of force application.
3. The electronic signature verification method as described in claim 2, characterized in that, The collection of the force characteristics of each stroke of the user's signature during the electronic signature process includes: Collect the original force data of each stroke of the signature during the electronic signature process; The raw force data is preprocessed to obtain preprocessed data; Extract the strength features of each signature stroke order from the preprocessed data.
4. The electronic signature verification method as described in claim 1, characterized in that, The determination of the similarity in strength between each signature stroke order and each standard signature stroke order based on the strength characteristics of each signature stroke order and the standard strength characteristics of each standard signature stroke order includes: Based on the strength characteristics of each signature stroke order, feature vectors for each signature stroke order are constructed, and based on the standard strength characteristics of each standard signature stroke order, feature vectors for each standard signature stroke order are constructed. Based on the feature vectors of each signature stroke order and each standard signature stroke order, the strength similarity between each signature stroke order and each standard signature stroke order is calculated using a preset strength similarity model.
5. The electronic signature verification method as described in any one of claims 1 to 4, characterized in that, Before determining the similarity in strength between each signature stroke order and each standard signature stroke order based on the strength characteristics of each signature stroke order and the standard strength characteristics of each standard signature stroke order, the method further includes: Obtain the user information of the user; Match the standard signature stroke order corresponding to the user information in the preset signature library; Search the preset force pattern database for the standard force feature corresponding to the standard signature stroke order.
6. The electronic signature verification method as described in claim 5, characterized in that, Before collecting the strength characteristics of each signature stroke order during the electronic signature process, the method also includes: Collect electronic signature samples from the users and construct a training dataset based on the electronic signature samples; The training dataset is learned by a machine learning model to construct a preset signature library and a preset strength pattern database.
7. An electronic signature verification device, characterized in that, The electronic signature verification device includes: The feature acquisition module is used to collect the strength characteristics of each stroke of the user's signature during the electronic signature process. The feature calculation module is used to determine the strength similarity between each signature stroke order and each standard signature stroke order based on the strength characteristics of each signature stroke order and the standard strength characteristics of each standard signature stroke order. The signature verification module is used to calculate the overall similarity of the electronic signature based on the strength similarity, and to verify the electronic signature according to the overall similarity.
8. An electronic signature verification device, characterized in that, The electronic signature verification device includes: a memory, a processor, and an electronic signature verification program stored in the memory and executable on the processor. When the electronic signature verification program is executed by the processor, it implements the electronic signature verification method as described in any one of claims 1 to 6.
9. A storage medium, characterized in that, The storage medium stores an electronic signature verification program, which, when executed by a processor, implements the electronic signature verification method as described in any one of claims 1 to 6.
10. A computer program product, characterized in that, The computer program product includes an electronic signature verification program, which, when executed by a processor, implements the electronic signature verification method as described in any one of claims 1 to 6.