Fraud information identification method and device, electronic equipment, storage medium and program product

Abstract

The invention provides a fraud information identification method and apparatus, an electronic device, a storage medium and a program product. The method comprises the steps of obtaining fraud propaganda description information; analyzing the fraud propaganda description information to obtain an analysis result; on the basis of the analysis result, a cue word is constructed; testing the cue word based on the first large language model to obtain a test result, and optimizing the cue word based on the test result to obtain an optimized cue word; reasoning the optimized cue word based on a second large language model to generate a fraud information sample; information to be recognized is obtained, the information to be recognized is input into a fraud information recognition model, a fraud information recognition result output by the fraud information recognition model is obtained, and the fraud information recognition model is obtained based on fraud information sample training. According to the method, the fraud sample is automatically generated by using the large language model, and the efficiency and accuracy of an anti-fraud technology for dealing with a novel fraud means are improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of fraud information identification, and in particular to a fraud information identification method, device, electronic device, storage medium and program product. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the disclosure that are recited in the claims. No description herein is admitted to be prior art by inclusion in this section.

[0003] With the popularization of the Internet and digital communications, the forms and methods of fraudulent activities have continued to evolve, posing serious security risks. Fraudulent activities are usually highly concealed and deceptive, which makes it increasingly difficult to manually collect and analyze fraud samples in related technologies. In order to improve the effectiveness and accuracy of fraud detection systems, researchers and practitioners need a large number of fraud samples for training and testing machine learning models.

[0004] In related technologies, the collection and analysis of fraud samples usually rely on manual operations, which is not only time-consuming and labor-intensive, but also difficult to keep up with the rapid changes in fraud methods. Summary of the invention

[0005] In view of this, the purpose of the present disclosure is to propose a fraud information identification method, device, electronic device, storage medium and program product, which at least to a certain extent solve one of the technical problems in the related art.

[0006] Based on the above purpose, the first aspect of the exemplary embodiment of the present disclosure provides a method for identifying fraudulent information, including:

[0007] Obtain information describing fraudulent propaganda;

[0008] Analyze the fraudulent promotion description information to obtain analysis results;

[0009] Based on the analysis results, construct prompt words;

[0010] Testing the prompt word based on the first language model to obtain a test result, and optimizing the prompt word based on the test result to obtain an optimized prompt word;

[0011] Inferring the optimized prompt words based on the second largest language model to generate fraud information samples;

[0012] Obtain information to be identified, input the information to be identified into a fraud information identification model, and obtain a fraud information identification result output by the fraud information identification model, wherein the fraud information identification model is trained based on the fraud information sample.

[0013] Based on the same inventive concept, the second aspect of the exemplary embodiment of the present disclosure provides a fraud information identification device, including:

[0014] A fraud propaganda description information acquisition module is configured to acquire fraud propaganda description information;

[0015] A fraudulent publicity description information analysis module is configured to analyze the fraudulent publicity description information to obtain an analysis result;

[0016] A prompt word construction module, configured to construct a prompt word based on the analysis result;

[0017] a prompt word optimization module, configured to test the prompt word based on the first language model to obtain a test result, and optimize the prompt word based on the test result to obtain an optimized prompt word;

[0018] a fraud information sample generating module, configured to infer the optimized prompt words based on the second largest language model to generate a fraud information sample;

[0019] The fraud information identification module is configured to obtain information to be identified, input the information to be identified into a fraud information identification model, and obtain a fraud information identification result output by the fraud information identification model, wherein the fraud information identification model is trained based on the fraud information sample.

[0020] Based on the same inventive concept, a third aspect of the exemplary embodiment of the present disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the first aspect is implemented.

[0021] Based on the same inventive concept, a fourth aspect of the exemplary embodiments of the present disclosure provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method described in the first aspect.

[0022] Based on the same inventive concept, a fifth aspect of the exemplary embodiments of the present disclosure provides a computer program product, including computer program instructions. When the computer program instructions are executed on a computer, the computer executes the method described in the first aspect.

[0023] From the above, it can be seen that the fraud information identification method, device, electronic device, storage medium and program product provided by the embodiment of the present disclosure include: obtaining fraud propaganda description information; analyzing the fraud propaganda description information to obtain analysis results; constructing prompt words based on the analysis results; testing the prompt words based on the first language model to obtain test results, optimizing the prompt words based on the test results to obtain optimized prompt words; reasoning the optimized prompt words based on the second language model to generate fraud information samples; obtaining information to be identified, inputting the information to be identified into the fraud information identification model, and obtaining the fraud information identification results output by the fraud information identification model, wherein the fraud information identification model is trained based on the fraud information samples. The present disclosure uses a large language model to realize the automatic generation of fraud samples, which improves the efficiency and accuracy of anti-fraud technology in dealing with new fraud methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A schematic diagram of an application scenario of a fraud information identification method provided by an exemplary embodiment of the present disclosure;

[0026] Figure 2 A flowchart of a fraud information identification method provided for an exemplary embodiment of the present disclosure;

[0027] Figure 3 A schematic diagram of a structure of a fraud information identification device provided for an exemplary embodiment of the present disclosure;

[0028] Figure 4 A schematic structural diagram of an electronic device provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] It is understandable that before using the technical solutions disclosed in the embodiments of this application, the type, scope of use, usage scenarios, etc. of the personal information involved in this application should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0030] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present application according to the prompt message.

[0031] As an optional but non-limiting implementation, in response to receiving an active request from the user, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0032] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation method of the present application. Other methods that meet the relevant laws and regulations may also be applied to the implementation method of the present application.

[0033] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0034] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, the principles and spirit of the present disclosure will be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present disclosure, and are not intended to limit the scope of the present disclosure in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0035] In the present disclosure, it should be understood that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction rather than having any limiting meaning.

[0036] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connecting" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. The article "one" or "a" before an element does not exclude the existence of multiple such elements.

[0037] The principle and spirit of the present disclosure are explained in detail below with reference to several representative embodiments of the present disclosure.

[0038] As described in the background technology, with the popularization of the Internet and digital communications, the forms and methods of fraudulent activities have continued to evolve, posing serious security risks. Fraudulent activities are usually highly concealed and deceptive, which makes it increasingly difficult to manually collect and analyze fraud samples in related technologies. In order to improve the effectiveness and accuracy of fraud detection systems, researchers and practitioners need a large number of fraud samples for training and testing machine learning models.

[0039] In related technologies, the collection and analysis of fraud samples usually rely on manual operations, which is not only time-consuming and labor-intensive, but also difficult to keep up with the rapid changes in fraud methods.

[0040] With the rapid development of large language models, using these models to automatically generate fraud samples has become a new solution. These models can generate high-quality and diverse text samples based on existing data, simulate different types of fraud techniques and strategies, and thus provide rich data support for fraud detection systems.

[0041] However, the inventors of the present disclosure have found that the existing technologies usually face the following challenges when generating fraud samples based on large language models: how to accurately extract and update fraud keywords and patterns, how to design effective prompt words to generate high-quality fraud samples, and how to ensure that the generated samples can cover the latest fraud trends and methods.

[0042] In order to solve the above problems, the present disclosure provides a fraud information identification scheme, which specifically includes: obtaining fraud publicity description information; analyzing the fraud publicity description information to obtain analysis results; constructing prompt words based on the analysis results; testing the prompt words based on the first language model to obtain test results, optimizing the prompt words based on the test results to obtain optimized prompt words; reasoning the optimized prompt words based on the second language model to generate fraud information samples; obtaining information to be identified, inputting the information to be identified into a fraud information identification model, and obtaining a fraud information identification result output by the fraud information identification model, wherein the fraud information identification model is trained based on the fraud information sample. The present disclosure uses a large language model to realize the automatic generation of fraud samples, which improves the efficiency and accuracy of anti-fraud technology in dealing with new fraud methods.

[0043] The method for automatically generating fraud samples based on a large language model proposed in the present disclosure improves the generation effect of fraud samples and the diversity of data through systematic data processing and prompt word optimization.

[0044] After introducing the basic principles of the present disclosure, various non-limiting embodiments of the present disclosure are described in detail below.

[0045] refer to Figure 1 , which is a schematic diagram of an application scenario of the fraud information identification method provided by the exemplary embodiment of the present disclosure.

[0046] This application scenario includes a terminal device 101, a server 102, and a data storage system 103. The terminal device 101, the server 102, and the data storage system 103 may be connected via a wired or wireless communication network to achieve data interaction.

[0047] The terminal device 101 may be an electronic device with data transmission and multimedia input / output functions close to the user side, including but not limited to a desktop computer, a mobile phone, a mobile computer, a tablet computer, a media player, a smart wearable device, a personal digital assistant (PDA) or other electronic devices capable of realizing the above functions. The electronic device may include a processor and a display screen with a touch input function, the display screen is used to present a graphical user interface, the graphical user interface can display an application interface, and the processor is used to process application data, generate a graphical user interface, and control the display of the graphical user interface on the display screen.

[0048] Server 102 and data storage system 103 can both be independent physical servers, or a server cluster or distributed system composed of multiple physical servers. They can also be cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0049] In some exemplary embodiments, the fraud information identification method may be run on the terminal device 101 or the server 102 .

[0050] When the fraud information identification method runs on the server 102, the server 102 is used to provide fraud information identification services to the user of the terminal device 101. The terminal device 101 is installed with a client that communicates with the server 102. The client can actively capture the information to be identified from the terminal device 101, and the user can also input the information to be identified through the client. The client sends the information to be identified to the server 102, and the server 102 obtains the information to be identified, and inputs the information to be identified into the fraud information identification model to obtain the fraud information identification result output by the fraud information identification model, wherein the fraud information identification model is trained based on the fraud information sample.

[0051] Among them, the method for obtaining the fraud information sample includes: obtaining fraud promotion description information; analyzing the fraud promotion description information to obtain analysis results; constructing prompt words based on the analysis results; testing the prompt words based on the first largest language model to obtain test results, optimizing the prompt words based on the test results to obtain optimized prompt words; inferring the optimized prompt words based on the second largest language model to generate fraud information samples.

[0052] After obtaining the fraud information identification result, the server 102 generates a fraud information warning message in response to determining that the fraud information identification result is that the information to be identified is fraud information, and sends the fraud information warning message to the client of the terminal device 101. The client displays the fraud information warning message to the user to help the user identify fraud.

[0053] The data storage system 103 stores fraud information samples. The server 102 can train the fraud information recognition model based on the fraud information samples so that the fraud information recognition model can recognize the input information to be recognized. When the accuracy of the fraud information recognition model output reaches a certain requirement, the server 102 can provide fraud information recognition services to users based on the fraud information recognition model. At the same time, the server 102 can also continuously optimize the fraud information recognition model based on the newly added fraud information samples.

[0054] Combine the following Figure 1 The training method of the fraud information identification model and the fraud information identification method according to the exemplary embodiment of the present disclosure are described by using the application scenario. It should be noted that the above application scenario is only shown to facilitate understanding of the spirit and principle of the present disclosure, and the embodiments of the present disclosure are not limited in this respect. On the contrary, the embodiments of the present disclosure can be applied to any applicable scenario.

[0055] refer to Figure 2 , which is a flow chart of a fraud information identification method provided by an exemplary embodiment of the present disclosure.

[0056] The method for identifying fraudulent information includes the following steps:

[0057] Step S210, obtaining fraudulent publicity description information.

[0058] In this exemplary embodiment, the sources of the fraudulent propaganda description information include but are not limited to existing databases and data crawled from the Internet. Below, some exemplary sources are introduced:

[0059] Social media platforms: Collect fraud information reported by users or suspicious activities detected by the platforms themselves from mainstream social media platforms. Social media is an important channel for the spread of fraudulent means, so the data obtained from these platforms is usually highly timely.

[0060] Online forums and communities: Online forums, community websites and hacker forums are often the source of new fraud methods. The system can regularly crawl and monitor these websites to collect the latest fraud methods and cases.

[0061] News media: Fraud cases reported regularly are also an important source of data. These cases are investigated and verified by journalists and are usually more reliable and typical.

[0062] Public databases: For example, public fraud datasets published by governments or non-profit organizations, which may contain detailed fraud case analysis and statistical information.

[0063] Reports from security companies and research institutions: Many cybersecurity companies and research institutions regularly publish research reports and white papers on fraud methods. These documents usually contain detailed fraud analysis and response strategies, and are an important source of high-quality data collection.

[0064] Through the above exemplary embodiments, the latest fraud propaganda description information can be collected from various public information sources or reliable data sources, ensuring that the solution provided by the present disclosure is always timely and highly realistic, so that when facing new types of fraud methods, it will be able to maintain a high level of defense capability and response effect, thereby better protecting users and systems from fraud threats.

[0065] Step S220: Analyze the fraudulent promotional description information to obtain analysis results.

[0066] In this exemplary implementation, before analyzing the fraudulent publicity description information, the fraudulent publicity description information is preprocessed, specifically:

[0067] The fraudulent publicity description information is cleaned to obtain cleaned fraudulent publicity description information.

[0068] In the specific implementation, in order to ensure the consistency of data, the system needs to clean the text data from different sources and standardize the data format. The content similarity algorithm is used to detect and delete duplicate text data. The existence of redundant data may affect the accuracy of the analysis results, so it must be strictly handled.

[0069] The standardized contents include unified coding format and unified text structure:

[0070] Unified encoding format, convert all text data into a unified character encoding (such as UTF-8) to avoid garbled characters caused by inconsistent encoding.

[0071] Unify the text structure. For data from different sources, unify the text structure, such as unified paragraph format, punctuation usage rules, etc. This can be achieved through regular expressions or text parsing tools. For data from different sources, unify the text structure, such as unified paragraph format, punctuation usage rules, etc. This can be achieved through regular expressions or text parsing tools.

[0072] Through the above exemplary embodiments, the fraudulent publicity description information is cleaned, which can improve the accuracy of analyzing the fraudulent publicity description information.

[0073] Preprocess the latest fraud text data collected to remove noise and irrelevant information, ensuring that the remaining text data is of high quality for further analysis.

[0074] Furthermore, duplicate or highly similar texts were deleted to avoid the influence of redundant data on subsequent analysis results. This step helps to improve the validity and representativeness of the data.

[0075] Then the text data from different sources are formatted in a unified way to ensure that the encoding, font, capitalization, etc. of all data meet the standards. This makes the subsequent analysis process smoother and reduces errors caused by inconsistent formats.

[0076] Through the above preprocessing steps, the system can ensure that the collected fraud text data is of high quality, uniformity and availability, providing a solid foundation for the subsequent prompt word design and generation of large language models.

[0077] In this exemplary embodiment, the analysis of the fraudulent publicity description information to obtain the analysis result includes:

[0078] Analyze the fraudulent means type on the fraudulent publicity description information to obtain a fraudulent means classification result;

[0079] Based on the fraud means classification result, natural language analysis is performed on the fraud propaganda description information to obtain feature information of the fraud propaganda description information;

[0080] Based on the classification results of the fraudulent means, a trend analysis is performed on the fraudulent publicity description information to obtain the priorities corresponding to different types of fraudulent means.

[0081] In specific implementation, different fraud methods are classified according to the text content, such as phishing scams, impersonating customer service, false investments, etc. Classification helps to identify the main characteristics and common methods of each type of fraud.

[0082] In specific implementation, the system uses natural language processing technology to extract common sentence structures, keywords, tone and style in fraudulent texts. These features will provide a basis for the subsequent prompt word design.

[0083] In practice, the system conducts trend analysis on the frequency of fraud methods, victim groups, and dissemination channels, and identifies which fraud methods are on the rise and which are gradually being eliminated. This helps ensure that the generated samples are consistent with the actual fraud dynamics.

[0084] Step S230: construct prompt words based on the analysis results.

[0085] In this exemplary embodiment, constructing prompt words based on the analysis results includes:

[0086] Based on the characteristic information of the fraudulent publicity description information and the priorities corresponding to the different types of fraudulent means, the prompt words for different fraud scenarios are constructed.

[0087] In specific implementation, based on the analysis of the latest fraud data, the initial design of prompt words should be combined with the current popular fraud methods and common fraud scenarios. The prompt words not only need to accurately capture the characteristics of fraud language, but also need to have a certain degree of flexibility in order to generate a variety of text samples.

[0088] In specific implementation, based on the analysis results, prompt word templates are designed for different fraud scenarios. The design of the template should have a certain degree of versatility and be able to adapt to different specific situations.

[0089] In practice, for some complex fraud scenarios, a single prompt word may not be able to fully cover all situations. In this case, you can design multi-segment or hierarchical prompt words to guide the model to generate more complex text samples.

[0090] In specific implementation, all valid prompt words are screened out according to the evaluation results, and the valid prompt words are logically combined to further enhance the prompt words.

[0091] Step S240: testing the prompt word based on the first language model to obtain a test result, and optimizing the prompt word based on the test result to obtain an optimized prompt word.

[0092] In specific implementation, after the prompt words are designed, the system will conduct preliminary tests on these prompt words to evaluate their generation effect and effectiveness in actual application.

[0093] In this exemplary embodiment, the step of testing the prompt word based on the first language model to obtain a test result, and optimizing the prompt word based on the test result to obtain an optimized prompt word includes:

[0094] Inputting the prompt word into the first language model to obtain a fraudulent text sample output by the first language model;

[0095] Performing effectiveness mixed correlation evaluation and diversity evaluation on the fraud text samples to obtain effectiveness mixed correlation evaluation results and diversity evaluation results;

[0096] Based on the effectiveness mixed correlation evaluation result and the diversity evaluation result, the prompt word is optimized to obtain the optimized prompt word.

[0097] In the specific implementation, each of the designed prompt words is used as input and fed into the large language model to generate a small number of fraudulent text samples, which are then analyzed and evaluated.

[0098] Quality evaluation of generated fraud samples is an important step in optimizing prompt words. This process directly affects the effectiveness of prompt words and the quality of the final generated samples. By evaluating the relevance, diversity, and effectiveness of generated samples, the system can identify the pros and cons of prompt words and make corresponding optimization adjustments to ensure that the large language model can generate high-quality fraud texts that meet expectations.

[0099] In specific implementation, the effectiveness hybrid correlation evaluation aims to ensure that the generated fraud samples are consistent with the expected content of the prompt words and can accurately reflect the fraud scenarios set by the prompt words. By checking whether the generated samples contain the keywords in the prompt words, it ensures that the generated content is consistent with the meaning of the prompt words. Figure 1 To.

[0100] In specific implementation, diversity assessment mainly examines the diversity of generated fraud samples in terms of content, style, and structure to avoid the generated text being too monotonous and repetitive. The improvement of diversity helps to simulate more types of fraud methods and improve the adaptability of the anti-fraud system. By checking the repetition rate between generated samples, it ensures that the system can generate rich and diverse texts.

[0101] Then, based on the generated results, identify the possible problems in the generation process of the prompt words, such as the generated text not meeting expectations, repeated content or lack of diversity.

[0102] In the specific implementation, further optimization and polishing are performed for the prompt words that meet the conditions, and the iterations are continued until the prompt words can generate effective samples.

[0103] Step S250: inferring the optimized prompt words based on the second largest language model to generate a fraud information sample.

[0104] In this exemplary embodiment, the inference of the optimized prompt words based on the second largest language model to generate a fraud information sample includes:

[0105] Get sample examples of fraudulent messages;

[0106] The fraud information sample example and the optimized prompt word are input into the second largest language model to obtain the fraud information sample output by the second largest language model.

[0107] In the specific implementation, in the process of generating fraud samples, a small amount of existing fraud text data is used as examples, and combined with the designed prompt words, the large language model can generate fraud samples that are consistent with the current fraud trends. This step is to organically combine the real fraud texts collected in the early stage with the optimized prompt words to give full play to the generation ability of the large language model and ensure that the generated samples are diverse and effective in content, style and structure.

[0108] In specific implementation, the sample data needs to be representative. The system selects a small representative sample set from the collected and processed fraud text data. These sample data usually include typical fraud cases, common fraud language patterns, and the latest fraud strategies. The selection of sample data is key, as it provides a realistic foundation for the large language model and ensures that the generated samples can closely fit the actual fraud scenario.

[0109] After selecting sample data, the system combines this data with previously optimized prompt words as input to the large language model. The optimized prompt words have been tested and adjusted to effectively guide the model to generate high-quality text samples. After combining with sample data, the guiding role of prompt words will be further enhanced, making the generated text more diverse and relevant.

[0110] In specific implementation, the system needs to filter out suitable sample data from the existing fraud text library. Such data should be representative and reflect the current common fraud methods and trends.

[0111] We also give priority to recent fraud cases to ensure that the data can reflect current fraud trends and provide a basis for the large language model to generate timely samples.

[0112] The design of prompt words is the key to guiding large language models to generate specific types of fraudulent texts, and combining prompt words with example data can enhance the realism and consistency of the generated results.

[0113] In the specific implementation, the selected sample data is embedded in the prompt words, and the large language model is guided to imitate the language style and logical structure of the sample data to generate fraudulent text with similar characteristics.

[0114] In actual operation, the generation process needs to go through certain management and optimization measures to ensure the quality and consistency of the generated samples.

[0115] During the specific implementation, the generation parameters of the large language model, such as temperature and maximum generation length, are adjusted according to the characteristics of the sample data and prompt words to control the diversity and logical consistency of the generated samples.

[0116] In addition, the best fraud samples that meet expectations can be gradually generated and screened out by batch generation, avoiding the generation of a large amount of low-quality text at one time and saving computing resources. During the generation process, the output quality of the model is monitored in real time, and the prompt words or generation parameters are adjusted when necessary to ensure high-quality output of the final sample.

[0117] After the large language model generates fraud samples, the generated results need to be preliminarily screened and evaluated to eliminate samples that do not meet the requirements and retain high-quality generated content.

[0118] In specific implementation, it is necessary to ensure that the generated samples meet the expected requirements of the sample data and prompt words in terms of logical structure, language style and content consistency. When screening, focus on the authenticity of the generated samples, that is, whether these texts are sufficiently deceptive and potentially confusing in reality. Eliminate duplicate or overly similar samples to ensure that the samples finally retained are diverse in content and form to meet the diverse needs of the anti-fraud system.

[0119] Through the above steps, the system can make full use of the existing fraud text data and the generation capabilities of the large language model, combined with carefully designed prompt words, to generate high-quality samples that conform to current fraud trends.

[0120] Step S260: obtaining information to be identified, inputting the information to be identified into a fraud information identification model, and obtaining a fraud information identification result output by the fraud information identification model, wherein the fraud information identification model is trained based on the fraud information sample.

[0121] In this exemplary embodiment, after obtaining the fraud information identification result output by the fraud information identification model, the method further includes:

[0122] In response to determining that the fraud information identification result is that the information to be identified is fraud information, a fraud information warning message is generated, and the fraud information warning message is sent to a designated terminal device.

[0123] In this exemplary embodiment, the method further includes:

[0124] Update the alerts regularly and adjust them based on new scams and strategies.

[0125] In the context of the rapid evolution of fraud methods, maintaining the timeliness and effectiveness of prompt words is the key to generating high-quality fraud samples. Regularly updating prompt words and adjusting them based on new fraud methods and strategies can ensure that the generated fraud samples always reflect the latest fraud trends and enhance the defense capabilities of the anti-fraud system.

[0126] In order to ensure the long-term effectiveness and adaptability of prompt words, the maintenance and management of the prompt word library is essential.

[0127] In specific implementation, regularly monitor and collect the latest fraud methods and trends, and identify emerging fraud patterns and strategies by analyzing the latest fraud texts and cases. Review existing prompts, identify parts that need to be updated, and adjust or expand them according to the latest fraud characteristics. Redesign and optimize prompts to ensure that they can cover emerging fraud scenarios and methods. Verify the effectiveness of the adjusted prompts, generate new samples, test and evaluate them, and ensure that they can accurately simulate the latest fraud strategies and maintain the high quality and diversity of generated samples.

[0128] In specific implementation, the prompt word library will be structured and managed according to the dimensions of fraud type, application scenario, etc., to facilitate the subsequent query, update and application of prompt words. Structural management can also improve the scalability of the prompt word library and support the addition of new fraud types in the future. The prompt word library will be structured and managed according to the dimensions of fraud type, application scenario, etc., to facilitate the subsequent query, update and application of prompt words. Structural management can also improve the scalability of the prompt word library and support the addition of new fraud types in the future.

[0129] Through the above mechanism, the system can regularly update and optimize the prompt word library to ensure that the generated fraud samples are always timely and highly realistic. In this way, the anti-fraud system will be able to maintain a high level of defense and response effectiveness when facing new fraud methods, thereby better protecting users and systems from fraud threats.

[0130] From the above, it can be seen that the present disclosure can use a large language model to realize the automatic generation of fraud samples, greatly improving the efficiency and accuracy of anti-fraud technology in dealing with new fraud methods. At the same time, the method of regularly updating and optimizing prompt words ensures the timeliness of sample generation, so that the anti-fraud system can always effectively defend against the latest fraud methods.

[0131] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0132] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0133] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a fraud information identification device.

[0134] refer to Figure 3 , which is a structural diagram of a fraud information identification device provided by an exemplary embodiment of the present disclosure.

[0135] The fraud information identification device includes the following modules:

[0136] The fraud publicity description information acquisition module 310 is configured to acquire fraud publicity description information;

[0137] The fraudulent publicity description information analysis module 320 is configured to analyze the fraudulent publicity description information to obtain an analysis result;

[0138] A prompt word construction module 330 is configured to construct a prompt word based on the analysis result;

[0139] The prompt word optimization module 340 is configured to test the prompt word based on the first language model to obtain a test result, and optimize the prompt word based on the test result to obtain an optimized prompt word;

[0140] A fraud information sample generating module 350 is configured to infer the optimized prompt words based on the second largest language model to generate a fraud information sample;

[0141] The fraud information identification module 360 ​​is configured to obtain information to be identified, input the information to be identified into a fraud information identification model, and obtain a fraud information identification result output by the fraud information identification model, wherein the fraud information identification model is trained based on the fraud information sample.

[0142] In some exemplary embodiments, the fraudulent promotion description information analysis module 320 is specifically configured to:

[0143] Analyze the fraudulent means type on the fraudulent publicity description information to obtain a fraudulent means classification result;

[0144] Based on the fraud means classification result, natural language analysis is performed on the fraud propaganda description information to obtain feature information of the fraud propaganda description information;

[0145] Based on the classification results of the fraudulent means, a trend analysis is performed on the fraudulent publicity description information to obtain the priorities corresponding to different types of fraudulent means.

[0146] In some exemplary embodiments, the prompt word construction module 330 is specifically configured to:

[0147] Based on the characteristic information of the fraudulent publicity description information and the priorities corresponding to the different types of fraudulent means, the prompt words for different fraud scenarios are constructed.

[0148] In some exemplary embodiments, the prompt word optimization module 340 is specifically configured to:

[0149] Inputting the prompt word into the first language model to obtain a fraudulent text sample output by the first language model;

[0150] Performing effectiveness mixed correlation evaluation and diversity evaluation on the fraud text samples to obtain effectiveness mixed correlation evaluation results and diversity evaluation results;

[0151] Based on the effectiveness mixed correlation evaluation result and the diversity evaluation result, the prompt word is optimized to obtain the optimized prompt word.

[0152] In some exemplary embodiments, the fraud information sample generation module 350 is specifically configured to:

[0153] Get sample examples of fraudulent messages;

[0154] The fraud information sample example and the optimized prompt word are input into the second largest language model to obtain the fraud information sample output by the second largest language model.

[0155] In some exemplary embodiments, the fraud information identification module 360 ​​is further configured to:

[0156] In response to determining that the fraud information identification result is that the information to be identified is fraud information, a fraud information warning message is generated, and the fraud information warning message is sent to a designated terminal device.

[0157] For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0158] The device of the above embodiment is used to implement the corresponding fraud information identification method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0159] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the fraud information identification method described in any of the above embodiments is implemented.

[0160] Figure 4 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0161] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0162] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0163] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0164] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0165] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0166] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0167] The electronic device of the above-mentioned embodiment is used to implement the corresponding fraud information identification method in any of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0168] The memory 1020 stores machine-readable instructions executable by the processor 1010. When the electronic device is running, the processor 1010 communicates with the memory 1020 via the bus 1030, so that the processor 1010 executes the following instructions when running:

[0169] Obtain information describing fraudulent propaganda;

[0170] Analyze the fraudulent promotion description information to obtain analysis results;

[0171] Based on the analysis results, construct prompt words;

[0172] Testing the prompt word based on the first language model to obtain a test result, and optimizing the prompt word based on the test result to obtain an optimized prompt word;

[0173] Inferring the optimized prompt words based on the second largest language model to generate fraud information samples;

[0174] Obtain information to be identified, input the information to be identified into a fraud information identification model, and obtain a fraud information identification result output by the fraud information identification model, wherein the fraud information identification model is trained based on the fraud information sample.

[0175] In a possible implementation manner, in the instructions executed by the processor 1010, the analyzing the fraudulent promotion description information to obtain the analysis result includes:

[0176] Analyze the fraudulent means type on the fraudulent publicity description information to obtain a fraudulent means classification result;

[0177] Based on the fraud means classification result, natural language analysis is performed on the fraud propaganda description information to obtain feature information of the fraud propaganda description information;

[0178] Based on the classification results of the fraudulent means, a trend analysis is performed on the fraudulent publicity description information to obtain the priorities corresponding to different types of fraudulent means.

[0179] In a possible implementation manner, in the instructions executed by the processor 1010, constructing a prompt word based on the analysis result includes:

[0180] Based on the characteristic information of the fraudulent publicity description information and the priorities corresponding to the different types of fraudulent means, the prompt words for different fraud scenarios are constructed.

[0181] In a possible implementation manner, in the instructions executed by the processor 1010, the step of testing the prompt word based on the first language model to obtain a test result, and optimizing the prompt word based on the test result to obtain an optimized prompt word includes:

[0182] Inputting the prompt word into the first language model to obtain a fraudulent text sample output by the first language model;

[0183] Performing effectiveness mixed correlation evaluation and diversity evaluation on the fraud text samples to obtain effectiveness mixed correlation evaluation results and diversity evaluation results;

[0184] Based on the effectiveness mixed correlation evaluation result and the diversity evaluation result, the prompt word is optimized to obtain the optimized prompt word.

[0185] In a possible implementation manner, in the instructions executed by the processor 1010, the inference of the optimized prompt words based on the second largest language model to generate a fraud information sample includes:

[0186] Get sample examples of fraudulent messages;

[0187] The fraud information sample example and the optimized prompt word are input into the second largest language model to obtain the fraud information sample output by the second largest language model.

[0188] In a possible implementation manner, in the instructions executed by the processor 1010, after obtaining the fraud information identification result output by the fraud information identification model, the method further includes:

[0189] In response to determining that the fraud information identification result is that the information to be identified is fraud information, a fraud information warning message is generated, and the fraud information warning message is sent to a designated terminal device.

[0190] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the fraud information identification method described in any of the above embodiments.

[0191] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0192] The above-mentioned non-transitory computer-readable storage medium can be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)), etc.

[0193] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the fraud information identification method described in any embodiment in the above exemplary method part, and have the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0194] Based on the same inventive concept, corresponding to the fraud information identification method described in any of the above embodiments, the present disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer so that the computer and / or the processor execute the fraud information identification method. Corresponding to the execution subject corresponding to each step in each embodiment of the fraud information identification method, the processor that executes the corresponding step can belong to the corresponding execution subject.

[0195] The computer program product of the above embodiment is used to enable the computer and / or the processor to execute the fraud information identification method described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0196] Those skilled in the art will appreciate that the embodiments of the present disclosure may be implemented as a system, method, or computer program product. Therefore, the present disclosure may be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, which is generally referred to as a "circuit," "module," or "system." In addition, in some embodiments, the present disclosure may also be implemented in the form of a computer program product in one or more computer-readable media, which contains computer-readable program code.

[0197] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive examples) of computer-readable storage media may include, for example: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device.

[0198] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0199] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0200] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0201] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified in the boxes in the flowchart and / or block diagram.

[0202] These computer program instructions may also be stored in a computer-readable medium that enables a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable medium produce a product that includes an instruction device that implements the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0203] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable device to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0204] In addition, although the operations of the disclosed method are described in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in this particular order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted in the flow chart can be performed in a different order. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0205] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0206] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0207] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0208] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0209] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0210] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

[0211] Although the spirit and principle of the present disclosure have been described with reference to several specific embodiments, it should be understood that the present disclosure is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined to benefit, and such division is only for the convenience of expression. The present disclosure is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the attached claims. The scope of the attached claims conforms to the broadest interpretation, thereby including all such modifications and equivalent structures and functions.

Claims

1. A method for identifying fraudulent information, characterized in that: include: Obtain information describing fraudulent propaganda; Analyze the fraudulent promotion description information to obtain analysis results; Based on the analysis results, construct prompt words; Testing the prompt word based on the first language model to obtain a test result, and optimizing the prompt word based on the test result to obtain an optimized prompt word; Inferring the optimized prompt words based on the second largest language model to generate fraud information samples; Obtain information to be identified, input the information to be identified into a fraud information identification model, and obtain a fraud information identification result output by the fraud information identification model, wherein the fraud information identification model is trained based on the fraud information sample.

2. The method according to claim 1, characterized in that The analysis of the fraudulent publicity description information to obtain analysis results includes: Analyze the fraudulent means type on the fraudulent publicity description information to obtain a fraudulent means classification result; Based on the fraud means classification result, natural language analysis is performed on the fraud propaganda description information to obtain feature information of the fraud propaganda description information; Based on the classification results of the fraudulent means, a trend analysis is performed on the fraudulent publicity description information to obtain the priorities corresponding to different types of fraudulent means.

3. The method according to claim 2, characterized in that The step of constructing prompt words based on the analysis result includes: Based on the characteristic information of the fraudulent publicity description information and the priorities corresponding to the different types of fraudulent means, the prompt words for different fraud scenarios are constructed.

4. The method according to claim 1, characterized in that: The step of testing the prompt word based on the first language model to obtain a test result, and optimizing the prompt word based on the test result to obtain an optimized prompt word includes: Inputting the prompt word into the first language model to obtain a fraudulent text sample output by the first language model; Performing effectiveness mixed correlation evaluation and diversity evaluation on the fraud text samples to obtain effectiveness mixed correlation evaluation results and diversity evaluation results; Based on the effectiveness mixed correlation evaluation result and the diversity evaluation result, the prompt word is optimized to obtain the optimized prompt word.

5. The method according to claim 1, characterized in that The inference of the optimized prompt words based on the second largest language model to generate a fraud information sample includes: Get sample examples of fraudulent messages; The fraud information sample example and the optimized prompt word are input into the second largest language model to obtain the fraud information sample output by the second largest language model.

6. The method according to claim 1, characterized in that After obtaining the fraud information identification result output by the fraud information identification model, the method further includes: In response to determining that the fraud information identification result is that the information to be identified is fraud information, a fraud information warning message is generated, and the fraud information warning message is sent to a designated terminal device.

7. A fraud information identification device, characterized in that: include: A fraud propaganda description information acquisition module is configured to acquire fraud propaganda description information; A fraudulent publicity description information analysis module is configured to analyze the fraudulent publicity description information to obtain an analysis result; A prompt word construction module, configured to construct a prompt word based on the analysis result; a prompt word optimization module, configured to test the prompt word based on the first language model to obtain a test result, and optimize the prompt word based on the test result to obtain an optimized prompt word; a fraud information sample generating module, configured to infer the optimized prompt words based on the second largest language model to generate a fraud information sample; The fraud information identification module is configured to obtain information to be identified, input the information to be identified into a fraud information identification model, and obtain a fraud information identification result output by the fraud information identification model, wherein the fraud information identification model is trained based on the fraud information sample.

8. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a computer to execute the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer program instructions, which, when executed on a computer, cause the computer to execute the method according to any one of claims 1 to 6.

Images