Large-model multi-dimensional automatic evaluation method based on dynamic confrontation evolution

Abstract

The invention discloses a large-model multi-dimensional automatic evaluation method based on dynamic adversarial evolution, which is characterized by comprising the following steps of: dynamic adversarial sample generation: generating a multi-modal adversarial sample including text, image and voice data through an adversarial evolution algorithm, simulating a hostile attack mode in a real environment, and generating a dynamic adversarial sample; triggering potential vulnerabilities of the large model; multi-dimensional evaluation index system construction: constructing a multi-dimensional quantitative evaluation system, automatically adapting to different industry requirements through a dynamic weight distribution algorithm, and generating a customized evaluation report; and automatic pressure test iteration: embedding a continuous evaluation module in a model development stage, and automatically generating an optimization strategy according to a test result in combination with a reinforcement learning technology.

Description

Technical Field

[0001] The present invention is applied to the field of artificial intelligence large models, and specifically relates to a multi-dimensional automated evaluation method for large models based on dynamic adversarial evolution. Background Art

[0002] Currently, the performance evaluation of artificial intelligence large models (such as models with tens of billions of parameters in the fields of natural language processing and computer vision) mainly relies on static test set verification or manual annotation evaluation. Such methods have significant limitations when dealing with dynamic adversarial attacks, complex scenario generalization, and multi-dimensional ethical compliance evaluation. With the in-depth application of large models in high-risk fields such as financial decision-making, medical diagnosis, and autonomous driving, problems such as adversarial data interference, algorithmic bias amplification, and lack of interpretability faced by models in real scenarios are becoming increasingly prominent. Due to the lack of the ability to construct a dynamic evolution test environment and a multi-index collaborative analysis mechanism, the traditional evaluation system is difficult to comprehensively reveal the performance degradation law of the model under continuous adversarial conditions, and it cannot meet the mandatory requirements of the artificial intelligence-related regulatory framework for model transparency and security. In this context, how to construct a model evaluation system that can simulate a dynamic adversarial environment, cover multi-dimensional evaluation indicators, and support automated closed-loop optimization has become the core technical bottleneck restricting the safe and reliable deployment of large models. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a multi-dimensional automated evaluation method for large models based on dynamic adversarial evolution in view of the deficiencies of the prior art.

[0004] To solve the above technical problem, a multi-dimensional automated evaluation method for large models based on dynamic adversarial evolution of the present invention includes the following steps:

[0005] Dynamic adversarial sample generation: Generate multi-modal adversarial samples, including text, image, and voice data, through an adversarial evolution algorithm to simulate malicious attack patterns in the real environment and trigger potential vulnerabilities of the large model;

[0006] Construction of a multi-dimensional evaluation index system: Construct a quantitative evaluation system including multiple dimensions, and automatically adapt to different industry requirements through a dynamic weight allocation algorithm to generate a customized evaluation report;

[0007] Automated stress test iteration: Embed a continuous evaluation module in the model development stage, and combine reinforcement learning technology to automatically generate optimization strategies according to the test results.

[0008] As a possible implementation manner, further, it also includes: Integration of a compliance review interface: Provide a standardized API interface to seamlessly connect with a third-party regulatory platform, and output the quantitative results of the model's compliance in privacy protection and ethical norms in real time to meet the requirements of industry access review.

[0009] As a possible implementation, further, the dynamic adversarial sample generation step includes:

[0010] Multi-modal adversarial sample generation: Integrate multiple adversarial attack algorithms, including at least Fast Gradient Sign Method (FGSM), Carlini & Wagner (CW) attack, and DeepFool, to generate adversarial samples of various data types such as text, images, and speech;

[0011] Industry scenario template library construction: Build an adversarial scenario template library according to industry characteristics, and dynamically update the template library to ensure its adaptation to the latest threat environment;

[0012] Dynamic evolution strategy engine: By monitoring the model's response to adversarial samples in real time, dynamically adjust the generation strategy of adversarial samples, gradually enhance its aggressiveness, and achieve multi-objective optimization;

[0013] Interpretability guarantee and traceability: Record all relevant parameters and process information of the adversarial sample generation process to ensure the transparency and traceability of the generation process.

[0014] As a possible implementation, further, the multi-dimensional evaluation index system construction step includes:

[0015] Robustness evaluation: Verify the stability of the model through adversarial sample attacks, and evaluate the degree of accuracy decline of the model under adversarial interference;

[0016] Fairness evaluation: Test the classification accuracy difference of the model among different user groups to ensure fair treatment of all groups by the model;

[0017] Interpretability evaluation: Use visualization techniques to analyze the attention distribution of the model, evaluate its attention to input features, and ensure the transparency and comprehensibility of the model's decision-making process;

[0018] Response consistency evaluation: Test the output consistency of the model under different inputs and application scenarios to ensure the stable performance of the model.

[0019] As a possible implementation, further, the automated stress test iteration step includes:

[0020] Evaluation result analysis: Receive the evaluation report generated by the multi-dimensional evaluation index system, and deeply analyze the specific performance scores and risk level identifiers of the model in each dimension;

[0021] Optimization strategy generation: Based on the output of the evaluation result analysis, use a technical means that combines reinforcement learning algorithms and genetic algorithms to automatically generate optimization strategies;

[0022] Model parameter adjustment: Automatically adjust the parameter settings of the model according to the generated optimization strategy, such as learning rate, regularization parameter, attention weight, etc.;

[0023] Model structure optimization: Optimize the neural network structure of the model through genetic algorithm to generate a more adaptable model configuration;

[0024] Optimization result feedback: Re-enter the optimized model into the dynamic adversarial sample generation engine for testing, and evaluate it again through the multi-dimensional evaluation index system to form a closed loop of continuous optimization.

[0025] As a possible implementation, further, the compliance review interface integration step includes:

[0026] Standardized API interface: Provide a standardized API interface to seamlessly connect with third-party regulatory platforms and output the quantitative compliance results of the model in dimensions such as privacy protection and ethical norms in real time;

[0027] Real-time monitoring and feedback: Real-time monitor the performance of the model under adversarial sample attacks, dynamically adjust the generation strategy of adversarial samples, and ensure the security of the model in complex adversarial environments;

[0028] Compliance report generation: Generate an evaluation report that meets industry standards, which is convenient for third-party regulatory platforms to call and review, and meets the requirements of high-compliance industries.

[0029] A multi-dimensional automated evaluation system for large models based on dynamic adversarial evolution includes the following modules:

[0030] Dynamic adversarial sample generation module: Used to generate multi-modal adversarial samples through adversarial evolution algorithms, including text, image, and voice data, simulate malicious attack patterns in real environments, and trigger potential vulnerabilities of large models in tasks such as natural language understanding and image recognition;

[0031] Multi-dimensional evaluation index system module: Used to construct a quantitative evaluation system including multiple dimensions such as robustness, fairness, interpretability, and response consistency, and automatically adapt to different industry requirements through dynamic weight allocation algorithms to generate customized evaluation reports;

[0032] Automated stress test iteration module: Used to embed a continuous evaluation module during the model development stage, combine reinforcement learning technology, automatically generate optimization strategies according to the test results, realize the evaluation-fix closed loop, and accelerate the security iteration of the model;

[0033] Compliance review interface module: Used to provide a standardized API interface to seamlessly connect with third-party regulatory platforms and output the quantitative compliance results of the model in dimensions such as privacy protection and ethical norms in real time, meeting the requirements of industry access reviews.

[0034] As a possible implementation, further, the dynamic adversarial sample generation module includes:

[0035] Multimodal adversarial sample generation unit: used to integrate multiple adversarial attack algorithms, including Fast Gradient Sign Method, Carlini & Wagner attack, and DeepFool, to generate adversarial samples of various data types such as text, images, and voices;

[0036] Industry scenario template library construction unit: used to construct an adversarial scenario template library according to industry characteristics, and dynamically update the template library to ensure its adaptation to the latest threat environment;

[0037] Dynamic evolution strategy engine unit: used to dynamically adjust the generation strategy of adversarial samples by monitoring the model's response to adversarial samples in real time, gradually enhance its aggressiveness, and achieve multi-objective optimization;

[0038] Interpretability guarantee and traceability unit: used to record all relevant parameters and process information in the adversarial sample generation process to ensure the transparency and traceability of the generation process.

[0039] As a possible implementation, further, the multi-dimensional evaluation index system module includes:

[0040] Robustness evaluation unit: used to verify the stability of the model through adversarial sample attacks and evaluate the degree of accuracy decline of the model under adversarial interference;

[0041] Fairness evaluation unit: used to test the classification accuracy difference of the model among different user groups to ensure fair treatment of all groups by the model;

[0042] Interpretability evaluation unit: used to analyze the attention distribution of the model using visualization techniques, evaluate its attention degree to input features, and ensure the transparency and comprehensibility of the model's decision-making process;

[0043] Response consistency evaluation unit: used to test the output consistency of the model under different inputs and application scenarios to ensure the stable performance of the model.

[0044] As a possible implementation, further, the automated stress test iteration module includes:

[0045] Evaluation result analysis unit: used to receive the evaluation report generated by the multi-dimensional evaluation index system, and deeply analyze the specific performance scores and risk level identifications of the model in each dimension;

[0046] Optimization strategy generation unit: used to automatically generate optimization strategies based on the output of the evaluation result analysis through a technical means combining reinforcement learning algorithms and genetic algorithms;

[0047] Model parameter adjustment unit: used to automatically adjust the parameter settings of the model according to the generated optimization strategy, such as learning rate, regularization parameter, attention weight, etc.;

[0048] Model structure optimization unit: used to optimize the neural network structure of the model through genetic algorithm to generate a more adaptable model configuration;

[0049] Optimization result feedback unit: used to re-enter the optimized model into the dynamic adversarial sample generation engine for testing, and evaluate it again through the multi-dimensional evaluation index system to form a closed loop of continuous optimization.

[0050] The present invention adopts the above technical solutions and has the following beneficial effects:

[0051] 1. High security

[0052] By simulating real adversarial scenarios through the dynamic adversarial sample generation engine, the potential vulnerabilities of the model under continuous evolutionary attacks are comprehensively revealed, significantly improving the security of the model in complex adversarial environments. The dynamic weight allocation algorithm combined with multi-dimensional evaluation indicators can accurately identify the weak links of the model, ensuring the comprehensiveness and reliability of the evaluation results.

[0053] 2. Prevent model abuse and misuse

[0054] The present invention effectively prevents the risk of bias amplification and misuse of the model in different groups and different scenarios through a multi-dimensional evaluation system, especially the quantitative analysis of fairness, interpretability, and response consistency. For example, in the financial field, the fairness evaluation of the model can effectively identify algorithmic discrimination problems; in the medical field, the interpretability evaluation can ensure that the diagnostic results of the model conform to clinical logic.

[0055] 3. High-efficiency automated optimization ability

[0056] The automated feedback optimization module realizes the continuous optimization and performance improvement of the model through reinforcement learning and genetic algorithm. The closed-loop feedback mechanism of the evaluation results and the optimization strategy significantly shortens the model iteration cycle, reduces manual intervention, and improves the iteration efficiency of the model.

[0057] 4. Flexible industry adaptability

[0058] The multi-dimensional evaluation index system supports dynamic weight allocation and can generate customized evaluation reports according to the specific needs of different industries (such as anti-fraud ability in the financial field and diagnostic reliability in the medical field) to meet the diverse needs of high-compliance industries.

[0059] 5. Low-cost model evaluation and optimization

[0060] Compared with traditional static test set verification and manual annotation evaluation, the present invention significantly reduces the costs of model evaluation and optimization through an automated evaluation and optimization process. The combination of dynamic adversarial sample generation and a multi-dimensional evaluation index system reduces the dependence on a large amount of manually annotated data and expert reviews.

[0061] 6. Support for a wide range of application scenarios

[0062] The present invention is not only applicable to general fields such as natural language processing and computer vision, but can also be widely applied to high-value industries such as fintech, intelligent healthcare, autonomous driving, cloud computing, and government supervision. Its dynamic adversarial evolution and multi-dimensional evaluation mechanism can adapt to the complex requirements of different industries and have broad market application prospects.

[0063] 7. Promote the ethical compliance of the model

[0064] Through the quantitative analysis of the fairness, interpretability, and privacy protection capabilities of the model by a multi-dimensional evaluation system, the present invention provides a scientific basis for the ethical compliance of the model and helps enterprises and society avoid legal risks and ethical disputes caused by issues such as model bias and privacy leakage.

[0065] 8. Enhance the commercial value of the model

[0066] Through comprehensive evaluation and continuous optimization, the present invention can significantly improve the performance and security of large models, making them more competitive in commercial deployment. Especially in industries with high compliance requirements, the high robustness, fairness, and interpretability of the model provide a solid technical guarantee for the commercial applications of enterprises.

[0067] In summary, the present invention has significant advantages in aspects such as model security evaluation, automated optimization, industry adaptability, and ethical compliance, can effectively meet the security and trust requirements of high-value industries for AI models, provides strong technical support for the commercial deployment of large models, and has important economic value and social significance. Specific implementation manners

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below.

[0069] Example 1

[0070] A multi-dimensional automated evaluation method for large models based on dynamic adversarial evolution includes the following steps:

[0071] Dynamic adversarial sample generation: Generate multi-modal adversarial samples through an adversarial evolution algorithm, including text, image, and speech data, simulate malicious attack patterns in a real environment, and trigger potential vulnerabilities of large models;

[0072] Construction of Multi - dimensional Evaluation Index System: Construct a quantitative evaluation system covering multiple dimensions. Through a dynamic weight allocation algorithm, it automatically adapts to the needs of different industries and generates customized evaluation reports;

[0073] Automated Stress Testing Iteration: Embed a continuous evaluation module during the model development stage. Combining reinforcement learning techniques, it automatically generates optimization strategies based on test results.

[0074] Integration of Compliance Review Interface: Provide a standardized API interface for seamless connection with third - party regulatory platforms, and real - time output the quantitative results of the model's compliance in privacy protection and ethical norms to meet the requirements of industry access reviews.

[0075] The steps of generating dynamic adversarial samples include:

[0076] Multi - modal Adversarial Sample Generation: Integrate multiple adversarial attack algorithms, at least including Fast Gradient Sign Method (FGSM), Carlini & Wagner (CW) attack, and DeepFool, to generate adversarial samples of various data types such as text, images, and voices;

[0077] Construction of Industry Scenario Template Library: Construct an adversarial scenario template library according to industry characteristics and dynamically update the template library to ensure its adaptation to the latest threat environment;

[0078] Dynamic Evolution Strategy Engine: By monitoring the model's response to adversarial samples in real - time, dynamically adjust the generation strategy of adversarial samples, gradually enhance their aggressiveness, and achieve multi - objective optimization;

[0079] Interpretability Assurance and Traceability: Record all relevant parameters and process information during the generation process of adversarial samples to ensure the transparency and traceability of the generation process.

[0080] The steps of constructing the multi - dimensional evaluation index system include:

[0081] Robustness Evaluation: Verify the stability of the model through adversarial sample attacks and evaluate the degree of accuracy decline of the model under adversarial interference;

[0082] Fairness Evaluation: Test the classification accuracy differences of the model among different user groups to ensure fair treatment of all groups by the model;

[0083] Interpretability Evaluation: Use visualization techniques to analyze the attention distribution of the model and evaluate its attention to input features to ensure the transparency and understandability of the model's decision - making process;

[0084] Response Consistency Evaluation: Test the output consistency of the model under different inputs and application scenarios to ensure the stability of the model's performance.

[0085] The iterative steps of automated stress testing include:

[0086] Analysis of evaluation results: Receive the evaluation report generated by the multi-dimensional evaluation index system, and deeply analyze the specific performance scores and risk level identifiers of the model in each dimension;

[0087] Generation of optimization strategies: Based on the output of the analysis of evaluation results, use a technical means combining reinforcement learning algorithms and genetic algorithms to automatically generate optimization strategies;

[0088] Adjustment of model parameters: Automatically adjust the parameter settings of the model according to the generated optimization strategies, such as learning rate, regularization parameter, attention weight, etc.;

[0089] Optimization of model structure: Optimize the neural network structure of the model through genetic algorithms to generate a more adaptable model configuration;

[0090] Feedback on optimization results: Re-enter the optimized model into the dynamic adversarial sample generation engine for testing, and evaluate it again through the multi-dimensional evaluation index system to form a closed loop of continuous optimization.

[0091] The steps for integrating the compliance review interface include:

[0092] Standardized API interface: Provide a standardized API interface, seamlessly connect with third-party regulatory platforms, and output the quantitative compliance results of the model in dimensions such as privacy protection and ethical norms in real time;

[0093] Real-time monitoring and feedback: Real-time monitor the performance of the model under adversarial sample attacks, dynamically adjust the generation strategy of adversarial samples, and ensure the security of the model in complex adversarial environments;

[0094] Generation of compliance reports: Generate evaluation reports that meet industry standards, facilitate the invocation and review by third-party regulatory platforms, and meet the requirements of high-compliance industries.

[0095] A multi-dimensional automated evaluation system for large models based on dynamic adversarial evolution includes the following modules:

[0096] Dynamic adversarial sample generation module: Used to generate multi-modal adversarial samples through adversarial evolution algorithms, including text, image, and voice data, simulate malicious attack patterns in real environments, and trigger potential vulnerabilities of large models in tasks such as natural language understanding and image recognition;

[0097] Multi-dimensional evaluation index system module: Used to construct a quantitative evaluation system including multiple dimensions such as robustness, fairness, interpretability, and response consistency, and automatically adapt to different industry needs through a dynamic weight allocation algorithm to generate customized evaluation reports;

[0098] Automated Stress Testing Iteration Module: Used to embed a continuous evaluation module during the model development stage, combined with reinforcement learning technology, automatically generate optimization strategies based on test results, achieve an evaluation-fix closed-loop, and accelerate the safe iteration of the model;

[0099] Compliance Review Interface Module: Used to provide a standardized API interface, seamlessly connect with third-party regulatory platforms, and output real-time quantitative compliance results of the model in dimensions such as privacy protection and ethical norms, meeting the requirements of industry access reviews.

[0100] The Dynamic Adversarial Sample Generation Module includes:

[0101] Multi-modal Adversarial Sample Generation Unit: Used to integrate multiple adversarial attack algorithms, including Fast Gradient Sign Method, Carlini & Wagner attack, and DeepFool, to generate adversarial samples of various data types such as text, images, and voices;

[0102] Industry Scenario Template Library Construction Unit: Used to construct an adversarial scenario template library according to industry characteristics, and dynamically update the template library to ensure its adaptation to the latest threat environment;

[0103] Dynamic Evolution Strategy Engine Unit: Used to dynamically adjust the generation strategy of adversarial samples by monitoring the model's response to adversarial samples in real time, gradually enhance its aggressiveness, and achieve multi-objective optimization;

[0104] Interpretability Assurance and Traceability Unit: Used to record all relevant parameters and process information of the adversarial sample generation process, ensuring the transparency and traceability of the generation process.

[0105] The Multi-dimensional Evaluation Index System Module includes:

[0106] Robustness Evaluation Unit: Used to verify the stability of the model through adversarial sample attacks and evaluate the degree of accuracy decline of the model under adversarial interference;

[0107] Fairness Evaluation Unit: Used to test the classification accuracy difference of the model among different user groups to ensure fair treatment of all groups by the model;

[0108] Interpretability Evaluation Unit: Used to analyze the attention distribution of the model using visualization technology, evaluate its attention to input features, and ensure the transparency and comprehensibility of the model's decision-making process;

[0109] Response Consistency Evaluation Unit: Used to test the output consistency of the model under different inputs and application scenarios to ensure the stable performance of the model.

[0110] The Automated Stress Testing Iteration Module includes:

[0111] Evaluation Result Analysis Unit: It is used to receive the evaluation report generated by the multi-dimensional evaluation index system, and deeply analyze the specific performance scores and risk level identifiers of the model in each dimension;

[0112] Optimization Strategy Generation Unit: It is used to automatically generate optimization strategies based on the output of the evaluation result analysis through a technical means that combines reinforcement learning algorithms and genetic algorithms;

[0113] Model Parameter Adjustment Unit: It is used to automatically adjust the parameter settings of the model according to the generated optimization strategies, such as learning rate, regularization parameter, attention weight, etc.;

[0114] Model Structure Optimization Unit: It is used to optimize the neural network structure of the model through genetic algorithms to generate a more adaptable model configuration;

[0115] Optimization Result Feedback Unit: It is used to re-enter the optimized model into the dynamic adversarial sample generation engine for testing, and evaluate it again through the multi-dimensional evaluation index system to form a closed loop of continuous optimization.

[0116] Embodiment 2

[0117] A large model automated evaluation technology based on dynamic adversarial evolution and multi-dimensional quantitative evaluation solves the deficiencies of traditional evaluation methods in adversarial testing, cross-dimensional performance verification, and optimization synergy through dynamic adversarial sample generation, multi-index joint analysis, and closed-loop feedback optimization mechanisms. The specific technical solutions are as follows:

[0118] 1. Dynamic Adversarial Sample Generation Engine

[0119] Adopts a hierarchical architecture to achieve the automated generation and dynamic evolution of adversarial scenarios, including the following core functions and implementation methods:

[0120] 1) Multi-modal Adversarial Sample Generation

[0121] Integrates a variety of adversarial attack algorithms, including Fast Gradient Sign Method (FGSM), Carlini & Wagner (CW) attack, DeepFool, etc., to achieve the generation of adversarial samples for various data types such as text, images, and voices. Among them, each data modality has its specific generation strategy:

[0122] Text modality: Generates adversarial samples through semantic drift and keyword substitution to ensure triggering the misjudgment of the model without significantly changing the text semantics.

[0123] Image modality: Generates adversarial samples through pixel perturbation and feature substitution to ensure deceiving the model under the premise of being visually imperceptible.

[0124] Speech modality: Generate adversarial examples through spectrum modification and noise injection to ensure misrecognition is triggered without affecting the speech recognizability.

[0125] During the generation process, the system dynamically adjusts the attack strategy according to the characteristics of the data modality, introducing random perturbations and multi-strategy combinations to ensure the generated adversarial examples are diverse and representative.

[0126] 2) Construction of industry scenario template library

[0127] Store and manage adversarial scenario templates for different industries. The construction process of the template library includes the following steps:

[0128] Template classification and management: Classify templates according to industry characteristics. For example, templates for the financial industry may include financial term replacement, sensitive information attacks, etc., and templates for the medical industry may include medical term interference, diagnostic information tampering, etc.

[0129] Template dynamic update: Regularly collect and organize new industry attack patterns to dynamically update the template library to ensure it adapts to the latest threat environment.

[0130] Template matching and application: According to the evaluation target and model application scenario, intelligently match and apply the corresponding industry templates to generate adversarial examples that conform to industry characteristics.

[0131] In addition, the template library also supports optimization and expansion. Based on historical evaluation data and model feedback, continuously optimize the aggressiveness and applicability of the templates, and gradually expand the coverage of the template library.

[0132] 3) Dynamic evolution strategy engine

[0133] By monitoring the model's response to adversarial examples in real time, dynamically adjust the generation strategy of adversarial examples, gradually enhancing their aggressiveness, and achieve optimization through the following steps:

[0134] Real-time monitoring and feedback: Monitor the model's response to adversarial examples in real time, such as misjudgment rate, calculation time, etc., to obtain feedback information.

[0135] Adaptive optimization strategy: Adjust the adversarial example generation strategy according to the feedback information, such as increasing the perturbation amplitude, changing the attack target, etc., to gradually enhance the aggressiveness of the adversarial examples.

[0136] Multi-objective optimization: During the optimization process, comprehensively consider multiple objectives such as the aggressiveness, imperceptibility, and computational efficiency of adversarial examples to achieve multi-objective optimization.

[0137] Through this dynamic evolution mechanism, the engine can generate more challenging adversarial examples and comprehensively explore the potential defects of the model.

[0138] 4) Interpretability guarantee and traceability

[0139] To ensure the transparency and traceability of the adversarial sample generation process, interpretability guarantees and traceability functions are provided:

[0140] Generation process record: When generating adversarial samples, all relevant parameters and process information are recorded to ensure traceability.

[0141] Visualization of the attack path: The attack path and impact of adversarial samples are displayed through visualization tools to help understand the vulnerability of the model.

[0142] Defect location and analysis: Based on the generation process of adversarial samples and the model response, potential defects of the model are located and an analysis report is generated.

[0143] Interpretive output: An interpretive output of adversarial samples is provided, showing changes in key features, attack targets, etc., to facilitate understanding of their attack mechanisms.

[0144] 2. Multi-dimensional evaluation index system

[0145] By constructing an evaluation framework covering multi-dimensional indicators such as robustness, fairness, interpretability, and response consistency, and combining a dynamic weight allocation algorithm, a comprehensive quantitative evaluation of the model is achieved, providing a scientific basis for model optimization and compliance deployment.

[0146] 1) Definition of evaluation dimensions

[0147] The evaluation system covers the following key dimensions, and specific evaluation indicators are set for each dimension to ensure a comprehensive and detailed evaluation:

[0148] Robustness evaluation

[0149] This dimension focuses on the stability and anti-interference ability of the model in the face of adversarial attacks and data noise. The evaluation indicators include:

[0150] Adversarial sample attack success rate: Measures the degree of accuracy decline of the model under adversarial sample attacks.

[0151] Functional safety vulnerability detection: Identifies incorrect behaviors induced by malicious inputs in specific tasks of the model.

[0152] Output stability: Evaluates the output consistency of the model under input perturbations.

[0153] Fairness evaluation

[0154] This dimension ensures fair treatment of different groups by the model and avoids bias and discrimination. The evaluation indicators include:

[0155] Classification bias detection: Evaluates the difference in classification accuracy of the model among different groups (such as gender, race).

[0156] Data representativeness analysis: Detect whether there is under - representation or over - representation of a specific group in the training data.

[0157] Interpretability evaluation

[0158] This dimension measures the transparency and comprehensibility of the model's decision - making process. The evaluation metrics include:

[0159] Attention mechanism analysis: Evaluate the degree of attention of the model to input features when processing tasks.

[0160] Clarity of output explanation: Measure the comprehensibility of the interpretive output generated by the model.

[0161] Interpretability of decision boundary: Evaluate the clarity of the model's decision boundary and whether it conforms to human cognitive logic.

[0162] Response consistency evaluation:

[0163] This dimension evaluates the output consistency of the model under different inputs and application scenarios. The evaluation metrics include:

[0164] Output consistency: Evaluate the consistency performance of the model under the same input.

[0165] Scenario adaptability: Evaluate the consistency of the model's performance under different application scenarios.

[0166] User feedback consistency: Evaluate the output consistency of the model through user feedback.

[0167] 2) Evaluation methods

[0168] The present invention adopts a variety of evaluation methods, comprehensively considering the performance of each dimension to ensure the comprehensiveness and accuracy of the evaluation results:

[0169] Robustness evaluation method

[0170] By generating adversarial samples (such as using algorithms like FGSM, PGD, etc.) and testing the accuracy of the model, evaluate the stability of the model under adversarial sample attacks. At the same time, design attack vectors for specific tasks to test the functional safety vulnerabilities of the model.

[0171] Fairness evaluation method

[0172] Analyze the group distribution in the training data to determine whether there is under - representation or over - representation. Test the classification accuracy of the model on datasets of different groups to evaluate whether the model has biases.

[0173] Interpretability evaluation method

[0174] Use visualization techniques to analyze the attention distribution of the model and evaluate its degree of attention to input features. Determine whether the interpretive output generated by the model is clear and understandable through manual or automated means. Plot the decision boundary of the model and evaluate whether it conforms to human cognitive logic.

[0175] Response Consistency Evaluation Method

[0176] Test the output consistency of the model under the same input. Test the performance consistency of the model under different application scenarios. Collect data on the consistency evaluation of the model output through user feedback.

[0177] 3) Dynamic Weight Allocation Algorithm

[0178] To meet the needs of different industries, the present invention introduces a dynamic weight allocation algorithm, which automatically adjusts the weights of each evaluation dimension according to user requirements and the model application scenario. The specific steps are as follows:

[0179] Initial weight setting: Set the initial weights of each evaluation dimension according to the model application scenario and user requirements. For example, in the financial field, the weight of the fairness dimension may be higher; in the medical field, the weight of the interpretability dimension may be higher.

[0180] Weight adjustment: Dynamically adjust the weights of each evaluation dimension according to the model test results and user feedback. For example, if the model performs poorly in terms of fairness, the weight of the fairness dimension can be increased to ensure that subsequent evaluations pay more attention to this dimension.

[0181] Weight optimization: Further adjust the weights through an optimization algorithm to ensure that the evaluation results better meet the actual requirements. This algorithm comprehensively considers the importance of each dimension and its impact on the overall performance of the model to achieve the optimal allocation of weights.

[0182] 4) Comprehensive Evaluation and Report Generation

[0183] Weight the evaluation results of each dimension according to the weights calculated by the dynamic weight allocation algorithm to obtain the comprehensive evaluation score of the model. The generated evaluation report includes the following content:

[0184] Comprehensive evaluation result: It includes the specific performance of the model in each evaluation dimension and the comprehensive evaluation score, reflecting the overall performance of the model.

[0185] Risk level identification: According to the evaluation results, classify the risks of the model in each dimension to facilitate users to quickly identify potential problems.

[0186] Improvement suggestions: Put forward specific improvement suggestions for the problems found in the evaluation to guide the direction of model optimization.

[0187] Standardized output: The format of the evaluation report complies with industry standards, facilitating the invocation and review by third-party regulatory platforms and meeting the requirements of highly compliant industries.

[0188] 3. Automated Feedback Optimization Module

[0189] By closely integrating with the multi-dimensional evaluation index system, it can dynamically analyze evaluation results, automatically generate optimization strategies, adjust model parameters or optimize the model structure, forming a closed-loop optimization system. This not only improves the model's performance in multiple dimensions such as robustness, fairness, interpretability, and response consistency, but also significantly reduces manual intervention and speeds up the model iteration speed, including the following steps:

[0190] 1) Analysis of Evaluation Results

[0191] Receive the evaluation report generated by the multi-dimensional evaluation index system, and deeply analyze the specific performance scores and risk level identifications of the model in each dimension. By identifying the deficiencies of the model in aspects such as robustness, fairness, interpretability, and response consistency, it provides a scientific basis for subsequent optimization. This process ensures the accuracy and pertinence of the optimization strategy and avoids waste of resources caused by blind adjustment.

[0192] 2) Generation of Optimization Strategies

[0193] Based on the output of the evaluation result analysis, automatically generate optimization strategies through a technical means that combines reinforcement learning algorithms and genetic algorithms. The reinforcement learning algorithm is used to dynamically adjust model parameters to improve the model's robustness against adversarial sample attacks; while the genetic algorithm simulates the process of natural selection and genetic variation to optimize the model structure and generate a more adaptable model configuration.

[0194] 3) Model Parameter Adjustment

[0195] According to the generated optimization strategy, automatically adjust the parameter settings of the model. Through the built-in automated parameter tuning tool, it can efficiently search for and apply the optimal parameter combinations, such as learning rate, regularization parameter, attention weight, etc.

[0196] 4) Model Structure Optimization

[0197] The function focuses on optimizing the neural network structure of the model. Through the genetic algorithm, this sub-function simulates the process of natural selection and genetic variation to generate a more adaptable model structure.

[0198] 5) Feedback of Optimization Results

[0199] The optimized model re-enters the dynamic adversarial sample generation engine for testing and is evaluated again through the multi-dimensional evaluation index system. The evaluation results are real-time fed back to the automated feedback optimization module to form a closed-loop of continuous optimization.

[0200] Example 3

[0201] Evaluation and Optimization of Financial Anti-Fraud Model Based on Dynamic Adversarial Evolution

[0202] 1. Background Description

[0203] In the financial field, anti-fraud models need to process a vast amount of transaction data and identify potential fraud behaviors in real time. Due to the complexity and high value of financial transactions, models are prone to exposing security vulnerabilities when facing adversarial attacks (such as data poisoning, adversarial sample attacks), resulting in misjudgments or missed detections. For example, attackers may deceive the model by constructing adversarial samples (such as modifying transaction amounts, timestamps, or transaction descriptions), causing legitimate transactions to be marked as fraud or fraudulent transactions to be missed. To verify the robustness, fairness, and interpretability of the model, the present invention proposes a multi-dimensional automated evaluation method and system based on dynamic adversarial evolution, combined with an automated feedback optimization module, to achieve continuous optimization and secure and trustworthy deployment of the model.

[0204] 2. Implementation Steps

[0205] 1) Application of Dynamic Adversarial Sample Generation Engine

[0206] Adversarial Sample Generation: The dynamic adversarial sample generation engine generates various adversarial samples according to the characteristics of financial transaction data. For example, attackers may construct adversarial samples through minor semantic drifts (such as replacing financial terms) or numerical perturbations (such as adjusting transaction amounts).

[0207] Industry Scenario Template Application: The engine combines scenario templates in the financial industry (such as financial term replacement, sensitive information attacks, etc.) to generate more targeted adversarial samples.

[0208] Dynamic Adjustment Strategy: The engine monitors the model's response to adversarial samples in real time (such as misjudgment rate, calculation time, etc.), dynamically adjusts the attack strategy, and gradually enhances the aggressiveness of the adversarial samples.

[0209] 2) Evaluation Process of Multi-Dimensional Evaluation Index System

[0210] The generated adversarial samples are input into the financial anti-fraud model to evaluate its performance in the following dimensions:

[0211] Robustness Evaluation: Verify the stability of the model through adversarial sample attacks and evaluate the degree of accuracy decline of the model under adversarial interference.

[0212] Fairness Evaluation: Test the fraud detection performance of the model among different user groups (such as high-income users and low-income users) to ensure fair treatment of all groups by the model.

[0213] Interpretability assessment: Verify whether the interpretive output of the model for fraudulent transactions is clear and understandable, for example, whether it can clearly point out the key features of fraud.

[0214] Response consistency assessment: Test the output consistency of the model under different transaction scenarios (such as online payment, offline transaction) to ensure the stable performance of the model.

[0215] 3) Optimization process of the automated feedback optimization module

[0216] According to the evaluation report generated by the multi-dimensional evaluation index system, the automated feedback optimization module performs the following steps:

[0217] Analysis of evaluation results: Analyze the deficiencies of the model in terms of robustness, fairness, etc., and identify the optimization directions.

[0218] Generation of optimization strategies: Generate optimization strategies for the special needs of the financial field. For example, adjust the adversarial training parameters of the model to enhance robustness, or optimize the model structure to improve fairness for specific user groups.

[0219] Adjustment of model parameters: Automatically optimize the learning rate, regularization parameters, etc. of the model to enhance the model's defense ability against financial adversarial attacks.

[0220] Optimization of model structure: Optimize the neural network structure of the model through genetic algorithms to enhance its adaptability to complex financial scenarios.

[0221] Feedback of optimization results: Re-input the optimized model into the test to form a closed-loop optimization.

[0222] Example 4

[0223] Evaluation and Optimization of Medical Diagnosis Model Based on Dynamic Adversarial Evolution

[0224] 1. Background description

[0225] In the medical field, diagnostic models based on deep learning are widely used in medical image analysis (such as lung cancer screening, skin lesion recognition, etc.). However, when faced with adversarial interference (such as image noise, feature occlusion), these models may produce misdiagnosis or missed diagnosis, threatening the health of patients. For example, an attacker may add noise to a medical image or occlude key lesion features to generate adversarial samples to deceive the model's lesion recognition ability. To verify the robustness, interpretability, and clinical compliance of the model, the present invention proposes a multi-dimensional automated evaluation method and system based on dynamic adversarial evolution, combined with an automated feedback optimization module, to achieve continuous optimization and secure and reliable deployment of the model.

[0226] 2. Implementation steps

[0227] 1) Application of the dynamic adversarial sample generation engine

[0228] Adversarial sample generation: The dynamic adversarial sample generation engine generates various adversarial samples according to the characteristics of medical images. For example, an attacker may construct adversarial samples by adding noise to the image or occluding key regions (such as the tumor edge).

[0229] Industry scenario template application: The engine combines scenario templates in the medical industry (such as medical term interference, diagnosis information tampering, etc.) to generate more targeted adversarial samples.

[0230] Dynamic adjustment strategy: The engine monitors the model's response to adversarial samples in real time (such as misdiagnosis rate, calculation time, etc.), dynamically adjusts the attack strategy, and gradually enhances the aggressiveness of adversarial samples.

[0231] 2) Evaluation process of the multi-dimensional evaluation index system

[0232] The generated adversarial samples are input into the medical diagnosis model to evaluate its performance in the following dimensions:

[0233] Robustness evaluation: Verify the stability of the model through adversarial sample attacks, and evaluate the degree of decline in the diagnostic accuracy of the model under adversarial interference.

[0234] Fairness evaluation: Test the diagnostic performance of the model in different patient groups (such as different ages, genders, races), and ensure fair treatment of all patients by the model.

[0235] Interpretability evaluation: Verify whether the interpretive output of the model for lesions is clear and easy to understand, for example, whether it can clearly point out the key features of the lesions.

[0236] Response consistency evaluation: Test the output consistency of the model in different imaging scenarios (such as different devices, different lighting conditions), and ensure the stable performance of the model.

[0237] 3) Optimization process of the automated feedback optimization module

[0238] According to the evaluation report generated by the multi-dimensional evaluation index system, the automated feedback optimization module performs the following steps:

[0239] Analysis of evaluation results: Analyze the deficiencies of the model in terms of robustness, fairness, etc., and identify the optimization directions.

[0240] Generation of optimization strategies: Generate optimization strategies for the special needs in the medical field. For example, adjust the adversarial training parameters of the model to enhance robustness, or optimize the model structure to improve fairness for specific patient groups.

[0241] Model parameter adjustment: Automatically optimize the learning rate, regularization parameters, etc. of the model to enhance the model's defense ability against medical adversarial interference.

[0242] Model structure optimization: Optimize the neural network structure of the model through genetic algorithms to enhance its adaptability to complex medical scenarios.

[0243] Optimization result feedback: Re-input the optimized model into the test to form a closed-loop optimization.

[0244] The above are the embodiments of the present invention. For those of ordinary skill in the art, according to the teachings of the present invention, any equivalent changes, modifications, substitutions, and variations made within the scope of the patent application of the present invention without departing from the principles and spirit of the present invention shall fall within the scope of the present invention.

Claims

1. A multi-dimensional automated evaluation method for large models based on dynamic adversarial evolution, characterized in that, It includes the following steps: Dynamic adversarial sample generation: Generate multimodal adversarial samples through an adversarial evolution algorithm, including text, image, and speech data, simulate malicious attack patterns in a real environment, and trigger potential vulnerabilities in the large model; Construction of a multi-dimensional evaluation index system: Construct a quantitative evaluation system containing multiple dimensions, and through a dynamic weight allocation algorithm, automatically adapt to the needs of different industries and generate customized evaluation reports; Automated stress test iteration: Embed a continuous evaluation module during the model development stage, combine reinforcement learning techniques, and automatically generate optimization strategies according to the test results.

2. The multi-dimensional automatic evaluation method for large models based on dynamic adversarial evolution according to claim 1, wherein It also includes: Integration of compliance review interfaces: Provide standardized API interfaces, seamlessly connect with third-party regulatory platforms, and output real-time quantitative results of the model's compliance in privacy protection and ethical norms, meeting industry access review requirements.

3. A multi-dimensional automated evaluation method for large models based on dynamic adversarial evolution according to claim 1, characterized in that The dynamic adversarial sample generation step includes: Multimodal adversarial sample generation: Integrate multiple adversarial attack algorithms, at least including Fast Gradient Sign Method (FGSM), Carlini & Wagner (CW) attack, and DeepFool, to generate adversarial samples of various data types such as text, image, and speech; Construction of an industry scenario template library: Construct an adversarial scenario template library according to industry characteristics, and dynamically update the template library to ensure its adaptation to the latest threat environment; Dynamic evolution strategy engine: By monitoring the model's response to adversarial samples in real time, dynamically adjust the generation strategy of adversarial samples, gradually enhance its aggressiveness, and achieve multi-objective optimization; Interpretability guarantee and traceability: Record all relevant parameters and process information in the process of generating adversarial samples to ensure the transparency and traceability of the generation process.

4. A multi-dimensional automated evaluation method for large models based on dynamic adversarial evolution according to claim 1, characterized in that, The multi-dimensional evaluation index system construction step includes: Robustness evaluation: Verify the stability of the model through adversarial sample attacks, and evaluate the degree of accuracy decline of the model under adversarial interference; Fairness evaluation: Test the classification accuracy differences of the model among different user groups to ensure fair treatment of all groups by the model; Interpretability evaluation: Use visualization techniques to analyze the attention distribution of the model, evaluate its attention to input features, and ensure the transparency and comprehensibility of the model's decision-making process; Response consistency evaluation: Test the output consistency of the model under different inputs and application scenarios to ensure stable performance of the model.

5. The multi-dimensional automatic evaluation method for large models based on dynamic adversarial evolution according to claim 1, characterized in that, The automated stress test iteration step includes: Analysis of evaluation results: Receive the evaluation report generated by the multi-dimensional evaluation index system, and deeply analyze the specific performance scores and risk level identifications of the model in each dimension; Generation of optimization strategies: Based on the output of the evaluation result analysis, automatically generate optimization strategies through a combination of reinforcement learning algorithms and genetic algorithms; Model parameter adjustment: Automatically adjust the parameter settings of the model according to the generated optimization strategies, such as learning rate, regularization parameter, attention weight, etc.; Model structure optimization: Optimize the neural network structure of the model through genetic algorithms to generate a more adaptable model configuration; Feedback of optimization results: Re-enter the optimized model into the dynamic adversarial sample generation engine for testing, and evaluate it again through the multi-dimensional evaluation index system to form a continuous optimization loop.

6. The multi-dimensional automated evaluation method for large models based on dynamic adversarial evolution according to claim 2, wherein, The compliance review interface integration steps include: Standardized API interface: Provide a standardized API interface to seamlessly connect with third-party regulatory platforms and output the quantitative compliance results of the model in dimensions such as privacy protection and ethical norms in real time; Real-time monitoring and feedback: Monitor the performance of the model under adversarial sample attacks in real time, dynamically adjust the generation strategy of adversarial samples, and ensure the security of the model in complex adversarial environments; Compliance report generation: Generate evaluation reports that meet industry standards, facilitate the invocation and review by third-party regulatory platforms, and meet the requirements of high-compliance industries.

7. A multi-dimensional automated evaluation system for large models based on dynamic adversarial evolution, characterized in that, It includes the following modules: Dynamic adversarial sample generation module: Used to generate multi-modal adversarial samples through adversarial evolution algorithms, including text, image, and voice data, simulate malicious attack patterns in real environments, and trigger potential vulnerabilities of large models in tasks such as natural language understanding and image recognition; Multi-dimensional evaluation index system module: Used to construct a quantitative evaluation system including multiple dimensions such as robustness, fairness, interpretability, and response consistency. Through the dynamic weight allocation algorithm, it automatically adapts to the needs of different industries and generates customized evaluation reports; Automated stress test iteration module: Used to embed a continuous evaluation module in the model development stage, combine reinforcement learning techniques, automatically generate optimization strategies according to test results, realize the evaluation-fix closed loop, and accelerate the security iteration of the model; Compliance review interface module: Used to provide a standardized API interface to seamlessly connect with third-party regulatory platforms and output the quantitative compliance results of the model in dimensions such as privacy protection and ethical norms in real time, meeting the requirements of industry access reviews.

8. The multi-dimensional automated evaluation system for large models based on dynamic adversarial evolution according to claim 1, characterized in that, The dynamic adversarial sample generation module includes: Multi-modal adversarial sample generation unit: Used to integrate multiple adversarial attack algorithms, including Fast Gradient Sign Method, Carlini & Wagner attack, and DeepFool, to generate adversarial samples of various data types such as text, images, and voices; Industry scenario template library construction unit: Used to build an adversarial scenario template library according to industry characteristics, dynamically update the template library, and ensure its adaptation to the latest threat environment; Dynamic evolution strategy engine unit: Used to dynamically adjust the generation strategy of adversarial samples by monitoring the model's response to adversarial samples in real time, gradually enhance its aggressiveness, and achieve multi-objective optimization; Interpretability guarantee and traceability unit: Used to record all relevant parameters and process information in the process of generating adversarial samples to ensure the transparency and traceability of the generation process.

9. The multi-dimensional automated evaluation system for large models based on dynamic adversarial evolution according to claim 1, characterized in that The multi-dimensional evaluation index system module includes: Robustness evaluation unit: Used to verify the stability of the model through adversarial sample attacks and evaluate the degree of accuracy decline of the model under adversarial interference; Fairness evaluation unit: Used to test the classification accuracy differences of the model among different user groups to ensure fair treatment of all groups by the model; Interpretability evaluation unit: Used to analyze the attention distribution of the model using visualization techniques, evaluate its attention to input features, and ensure the transparency and understandability of the model's decision-making process; Response consistency evaluation unit: Used to test the output consistency of the model under different inputs and application scenarios to ensure the stable performance of the model.

10. A multi-dimensional automated evaluation system for large models based on dynamic adversarial evolution according to claim 1, characterized in that, The automated pressure test iteration module includes: Evaluation result analysis unit: It is used to receive the evaluation report generated by the multi-dimensional evaluation index system, and deeply analyze the specific performance scores and risk level identifications of the model in each dimension; Optimization strategy generation unit: It is used to automatically generate optimization strategies based on the output of the evaluation result analysis through a technical means combining reinforcement learning algorithms and genetic algorithms; Model parameter adjustment unit: It is used to automatically adjust the parameter settings of the model according to the generated optimization strategies, such as learning rate, regularization parameter, attention weight, etc.; Model structure optimization unit: It is used to optimize the neural network structure of the model through genetic algorithms to generate a more adaptable model configuration; Optimization result feedback unit: It is used to re-enter the optimized model into the dynamic adversarial sample generation engine for testing, and evaluate it again through the multi-dimensional evaluation index system to form a closed loop of continuous optimization.