Deep learning model evaluation and optimization platform and method based on multiple modes

Abstract

The invention discloses a multi-mode-based deep learning model evaluation and optimization platform, which adopts a layered architecture design and comprises a user interaction layer, a service processing layer and a data processing layer. The platform achieves model robustness evaluation through a three-stage workflow. In the initial configuration stage, evaluation task creation, authority verification, model / data set loading and attack parameter setting are completed. In the evaluation execution stage, computing resources are dynamically allocated, attack samples are generated, and model reasoning of the original samples and the attack samples is executed in parallel; in the result analysis stage, multi-dimensional evaluation indexes, visual charts and structured reports are generated, and robustness enhancement operations such as adversarial training and feature distillation are supported. According to the method, multiple attack methods such as an adversarial sample test and a noise interference test are integrated, an automatic evaluation closed loop from model verification, resource scheduling and attack simulation to multi-dimensional analysis is realized, and the efficiency and systematicness of robustness evaluation of the deep learning model are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of deep learning technology, and specifically to a multimodal deep learning model evaluation and optimization platform and method. Background Art

[0002] With the rapid development of deep learning technology, the application of deep learning models has become increasingly widespread, but the reliability assessment tools that have emerged have failed to keep pace. Based on market research and user demand analysis, existing assessment tools have the following shortcomings: First, the tool ecosystem is fragmented. For example, independent tool libraries such as RobustBench from the University of Tübingen team and ARES from the Tsinghua University and Alibaba teams exist separately. Users need to operate across platforms and manually integrate results, which increases operational complexity and time costs. Second, tool design has technical barriers, requiring users to have deep learning framework development and mathematical modeling capabilities, making it difficult for business personnel and decision-makers to directly participate in the assessment process. Third, the assessment process relies on manual intervention, covering data preprocessing, model loading, attack generation, and result analysis. This easily leads to the risk of operational errors and reduces assessment efficiency. Summary of the Invention

[0003] In response to the above problems, the present invention proposes a multimodal deep learning model evaluation and optimization platform, which includes a user interaction layer, a business processing layer and a data processing layer.

[0004] The user interaction layer is used to allow users to select the type of robustness evaluation task, the deep learning model to be evaluated, the dataset used for the evaluation task, the attack method and attack parameters, and display the progress of the evaluation task;

[0005] The business processing layer is used to: verify the format of the deep learning model to be evaluated; load the deep learning model to be evaluated; preprocess the data set and verify the attack parameters; allocate computing resources to the deep learning model to be evaluated; execute the attack method and generate attack samples; perform the inference process on the deep learning model to be evaluated and generate inference results; generate evaluation indicators and their visualization charts based on the inference results;

[0006] The data processing layer is used to: verify user permissions; store the deep learning model to be evaluated; store the preprocessed data set; store the attack method and the attack parameters; cache the attack samples and the inference results; and aggregate the evaluation indicators to construct a multi-dimensional analysis view.

[0007] Furthermore, the workflow of the platform is divided into three phases: initialization and configuration, evaluation execution, and result analysis and report presentation;

[0008] Furthermore, the specific process of the initialization and configuration phase is as follows:

[0009] In the user interaction layer, the user first selects the robustness evaluation type; then the business processing layer loads and creates a uniquely identified evaluation task;

[0010] After the evaluation task is created, the data processing layer verifies the user's permissions;

[0011] If the permission verification fails, the process is interrupted;

[0012] If the permission verification is passed, continue to execute the following process:

[0013] At the user interaction layer, users choose to upload a new deep learning model or a preset deep learning model; the deep learning model format is then verified at the business processing layer;

[0014] If the verification format is incorrect, the process is interrupted;

[0015] If the verification format is correct, the selected deep learning model is loaded into memory for evaluation and transferred to the data processing layer for storage;

[0016] In the user interaction layer, the user selects the dataset for the evaluation task and sets the sample size. The dataset is then preprocessed in the business processing layer and stored in the data processing layer.

[0017] At the user interaction layer, the user selects the attack method and attack parameters for the evaluation task, and then the attack parameters are checked for validity at the business processing layer. After passing the check, the attack method and attack parameters are stored in the data processing layer.

[0018] Furthermore, the specific process of the initialization and configuration phase is as follows:

[0019] After the initialization and configuration phase is completed, computing resources are allocated to the evaluation task at the business processing layer;

[0020] After the computing resources are ready, the business processing layer executes the attack method on the deep learning model based on the attack parameters, and generates a number of attack samples equal to the set sample size for the deep learning model;

[0021] Subsequently, at the business processing layer, the original samples in the preprocessed dataset and the generated attack samples are used to simultaneously perform deep learning model inference and collect the inference results;

[0022] During the entire initialization and configuration phase, the user interaction layer displays the progress of the evaluation task in real time, while the data processing layer is responsible for caching attack samples and inference results.

[0023] Furthermore, the specific process of the result analysis and report presentation stage is as follows:

[0024] The business processing layer performs in-depth processing on the collected inference results to generate evaluation indicators and also generates visualization charts of the evaluation indicators;

[0025] The data processing layer is responsible for aggregating evaluation indicators and building multi-dimensional analysis views;

[0026] The business processing layer automatically generates structured evaluation reports, including evaluation indicators and visual charts;

[0027] Users make robustness enhancement actions based on the structured evaluation report.

[0028] Furthermore, the robustness enhancement operation includes adversarial training strategies, feature distillation techniques and data enhancement schemes.

[0029] Furthermore, attack methods include adversarial sample testing, noise interference testing, and abnormal output testing.

[0030] Compared to existing solutions, this platform significantly reduces manual effort and improves the consistency of evaluation results by integrating automated evaluation processes with standardized metrics. This solution transcends the technical limitations of traditional tools, building comprehensive evaluation capabilities from model development to business decision-making, providing systematic technical support for the credibility assessment of deep learning models. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a platform architecture diagram of the present invention;

[0032] Figure 2 It is the platform workflow diagram of the present invention;

[0033] Figure 3 Schematic diagram a of the user interaction layer of the present invention;

[0034] Figure 4 Schematic diagram b of the user interaction layer of the present invention;

[0035] Figure 5 It is a schematic diagram of the analysis report of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] Embodiment 1:

[0038] This embodiment provides a multimodal deep learning model evaluation and optimization platform, which adopts a layered design architecture and clearly divides the responsibilities of the user interaction layer, business processing layer, and data processing layer. Figure 1 As shown,

[0039] The user interaction layer is used to provide users with the ability to select the evaluation task type, the deep learning model to be evaluated and the dataset required for its evaluation, the attack method and attack parameters, and to display the progress of the evaluation task.

[0040] The functions of the business processing layer include:

[0041] Verify deep learning model format;

[0042] Loading deep learning models;

[0043] Preprocess the dataset and verify attack parameters;

[0044] Allocate computing resources for deep learning models;

[0045] Execute attack methods and generate attack samples;

[0046] Execute deep learning model inference and generate inference results;

[0047] Generate evaluation metrics and their visualization charts based on the inference results.

[0048] The functions of the data processing layer include: verifying user permissions; storing the deep learning model to be evaluated; storing the preprocessed data set; storing the attack method and the attack parameters; caching the attack samples and the inference results; aggregating the evaluation indicators to construct a multi-dimensional analysis view

[0049] Example 2:

[0050] This embodiment provides the workflow of embodiment 1, such as Figure 2 As shown:

[0051] (1) Initialization and configuration phase

[0052] The workflow begins with the initialization phase.

[0053] In the user interaction layer, the user first selects the robustness assessment type (image or text); then the business processing layer loads and creates a uniquely identified assessment task; after the task is created, the data processing layer performs permission verification to ensure that the user has sufficient permissions to access the target resources.

[0054] At the user interaction layer, users can choose to upload a new deep learning model or select a preset deep learning model, and then the deep learning model format will be verified at the business processing layer. If the verification format is incorrect, the process will be interrupted; if the verification format is correct, the selected deep learning model will be loaded into the memory for evaluation and transferred to the data processing layer for storage.

[0055] In the user interaction layer, the user selects the dataset for the evaluation task and sets the sample size. The dataset is then preprocessed in the business processing layer and stored in the data processing layer.

[0056] In the user interaction layer, the user selects the attack method and attack parameters for the assessment task. The attack parameters are then validated in the business processing layer. If the validation passes, the attack method and attack parameters are stored in the data processing layer.

[0057] At this point, the initialization and configuration phase of the evaluation module is completed. The user interaction layer function is shown in Figure 3 and Figure 4 .

[0058] (2) Evaluation and implementation phase

[0059] The evaluation and execution phase constitutes the core operation process of this module.

[0060] After the initialization and configuration phase is completed, the necessary computing resources (CPU / GPU) are allocated to the evaluation task in the business processing layer to prepare for high-performance evaluation tasks.

[0061] After the computing resources are ready, at the business processing layer, the attack method is executed on the deep learning model in combination with the attack parameters, and attack samples equal to the set sample size are generated for the deep learning model.

[0062] In this embodiment, the attack methods include adversarial sample testing, noise interference testing, and abnormal output testing;

[0063] These samples are designed to detect vulnerable areas of the model's decision boundary and test its robustness under unexpected inputs.

[0064] Subsequently, at the service processing layer, deep learning model inference is performed simultaneously using the original samples in the dataset and the generated attack samples, and the inference results are collected. Model inference refers to the classification task corresponding to deep learning, such as image classification, which is not limited in this embodiment.

[0065] During the entire execution process, the user interaction layer displays the progress of the evaluation task in real time, while the data processing layer is responsible for caching attack samples and inference results, laying the foundation for subsequent analysis.

[0066] (3) Result analysis and report presentation stage

[0067] In the result analysis phase, the collected inference results are first deeply processed at the business processing layer, including calculating evaluation indicators such as the attack success rate of the attack method and the change in model inference confidence, to comprehensively quantify the robustness of the model.

[0068] At the same time, visual charts of evaluation indicators are generated at the business processing layer, including comparison charts and heat maps, which intuitively present the attack effects and model vulnerability distribution.

[0069] The data processing layer is responsible for aggregating evaluation indicators and building multi-dimensional analysis views.

[0070] In the final report presentation stage, the business processing layer automatically generates a structured evaluation report, see Figure 5 , covering evaluation indicators and visualization charts, users can fully understand the robustness of the model through the user interaction layer and make targeted optimization decisions.

[0071] Example 3:

[0072] When the evaluation indicators in Example 2 do not reach the set threshold, the user can choose to perform robustness enhancement operations on the deep learning model, including adversarial training strategies, feature distillation techniques and data enhancement schemes.

[0073] Adversarial training strategy: supports multiple standard adversarial training methods such as PGD adversarial training and TRADES, allowing users to adjust adversarial training parameters and the proportion of adversarial samples;

[0074] Feature distillation technology: Implements a model enhancement method based on knowledge distillation, extracts feature representations of robust models, and improves the original model's anti-interference ability;

[0075] Data augmentation solution: Provides automatic data augmentation based on adversarial samples, expands the training set, and improves the generalization and robustness of the model.

[0076] Experimental verification:

[0077] This platform uses a front-end and back-end separation architecture to build a model robustness evaluation system. Its development environment configuration is shown in Table 1. This platform uses common attack methods FGSM, PGD, TextFooler, and BertAttact to evaluate the robustness of ResNet-50 and BERT-base models, respectively. The results are shown in Table 2.

[0078] Table 1 Platform development environment configuration table

[0079]

[0080] Table 2 Robustness evaluation results

[0081]

[0082]

[0083] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

Claims

1. A multimodal deep learning model evaluation and optimization platform, comprising a user interaction layer, a business processing layer, and a data processing layer, characterized in that: The user interaction layer is used to allow users to select the type of robustness evaluation task, the deep learning model to be evaluated, the dataset used for the evaluation task, the attack method and attack parameters, and display the progress of the evaluation task; The business processing layer is used to: verify the format of the deep learning model to be evaluated; load the deep learning model to be evaluated; preprocess the data set and verify the attack parameters; and allocate computing resources to the deep learning model to be evaluated; Execute the attack method and generate attack samples; perform the inference process on the deep learning model to be evaluated and generate inference results; generate evaluation indicators and their visualization charts based on the inference results; The data processing layer is used to: verify user permissions; Storing the deep learning model to be evaluated; Storing the preprocessed data set; storing the attack method and the attack parameters; caching the attack samples and the inference results; The evaluation indicators are aggregated to construct a multi-dimensional analysis view.

2. The platform according to claim 1, characterized in that The workflow of the platform is divided into three stages: initialization and configuration, evaluation execution, and result analysis and report presentation.

3. The platform according to claim 2, characterized in that The specific process of the initialization and configuration phase is as follows: In the user interaction layer, the user first selects the robustness evaluation type; then the business processing layer loads and creates a uniquely identified evaluation task; After the evaluation task is created, the data processing layer verifies the user's permissions; If the permission verification fails, the process is interrupted; If the permission verification is passed, continue to execute the following process: At the user interaction layer, users choose to upload a new deep learning model or a preset deep learning model; the deep learning model format is then verified at the business processing layer; If the verification format is incorrect, the process is interrupted; If the verification format is correct, the selected deep learning model is loaded into memory for evaluation and transferred to the data processing layer for storage; In the user interaction layer, the user selects the dataset for the evaluation task and sets the sample size. The dataset is then preprocessed in the business processing layer and stored in the data processing layer. At the user interaction layer, the user selects the attack method and attack parameters for the evaluation task, and then the attack parameters are checked for validity at the business processing layer. After passing the check, the attack method and attack parameters are stored in the data processing layer.

4. The platform according to claim 3, characterized in that: The specific process of the initialization and configuration phase is as follows: After the initialization and configuration phase is completed, computing resources are allocated to the evaluation task at the business processing layer; After the computing resources are ready, the business processing layer executes the attack method on the deep learning model based on the attack parameters, and generates a number of attack samples equal to the set sample size for the deep learning model; Subsequently, at the business processing layer, the original samples in the preprocessed dataset and the generated attack samples are used to simultaneously perform deep learning model inference and collect the inference results; During the entire initialization and configuration phase, the user interaction layer displays the progress of the evaluation task in real time, while the data processing layer is responsible for caching attack samples and inference results.

5. The platform according to claim 4, characterized in that: The specific process of the result analysis and report presentation stage is as follows: The business processing layer performs in-depth processing on the collected inference results to generate evaluation indicators and also generates visualization charts of the evaluation indicators; The data processing layer is responsible for aggregating evaluation indicators and building multi-dimensional analysis views; The business processing layer automatically generates structured evaluation reports, including evaluation indicators and visual charts; Users make robustness enhancement actions based on the structured evaluation report.

6. The platform according to claim 5, characterized in that: The robustness enhancement operations include adversarial training strategies, feature distillation techniques, and data augmentation schemes.

7. The platform according to claim 1 or 4, characterized in that The attack methods include adversarial sample testing, noise interference testing and abnormal output testing.

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