Intelligent algorithm weakness discovery and iterative optimization method and system based on interpretable analysis

Abstract

The invention provides an intelligent algorithm weakness discovery and iterative optimization method and system based on interpretable analysis. On the basis of interpretability analysis, algorithm weak links are automatically positioned, component robustness is tested and evaluated through multi-dimensional systematization, key weak points influencing algorithm performance are efficiently recognized, a complete closed loop from weak point discovery to targeted optimization is established, and a universal method supporting multiple intelligent algorithm types is established. According to the method, component-level refined weak point positioning is achieved, limitation of sample-level coarse-grained analysis of a traditional method is broken through through semantic component decomposition and an independent test mechanism, and an accurate target is provided for targeted optimization. A complete closed-loop mechanism from weakness discovery to targeted optimization is constructed, the method comprises the links of weakness identification, optimization strategy generation, effect evaluation, iterative improvement and the like, and an efficient iterative optimization process is realized.

Description

A method and system for intelligent algorithm weakness discovery and iterative optimization based on interpretable analysis Technical Field

[0001] This invention belongs to the field of artificial intelligence and explainable AI technology, specifically relating to a method and system for discovering and iteratively optimizing the weaknesses of intelligent algorithms based on explainable analysis. Background Technology

[0002] With the rapid development of deep learning technology, intelligent algorithms have achieved remarkable results in the field of computer vision. However, these algorithms generally suffer from the following problems: (1) The "black box" nature of intelligent algorithms; deep learning models usually contain millions or even billions of parameters, and their internal decision-making mechanisms are complex and opaque. This "black box" nature leads to unexplainable decisions, difficulty in debugging, low user trust, and difficulty in meeting the interpretability regulatory requirements of some industries. (2) The problem of algorithm robustness; intelligent algorithms often exhibit vulnerability in real-world application scenarios, including sensitivity to adversarial attacks, performance degradation when distribution shifts, poor performance of long-tailed samples, and failure in specific scenarios. (3) Difficulty in discovering weaknesses; existing methods are difficult to systematically discover the weak links of algorithms, lack precise localization capabilities, have incomplete test coverage, are inefficient, and optimization measures are often general rather than targeted at specific weaknesses.

[0003] Existing interpretability methods mainly include feature importance analysis methods (such as LIME and SHAP), gradient-based methods (such as Grad-CAM and Integrated Gradients), and attention mechanisms. These methods are mainly used to explain model decisions, but they have the following limitations: high computational cost, can only provide coarse-grained explanations, are difficult to apply directly to systematic weakness discovery, and lack effective correlation with weakness localization.

[0004] Existing adversarial testing methods mainly include gradient-based attack methods (such as FGSM, PGD, and C&W), optimization-based methods (such as DeepFool and Universal Adversarial Perturbations), and black-box attack methods. These methods can generate adversarial examples to evaluate the robustness of algorithms, but they suffer from the following problems: they require a large number of iterations and queries, have high computational costs, use a single perturbation paradigm, are difficult to explain the reasons for failure, and cannot systematically identify weakness patterns.

[0005] Existing robustness enhancement methods mainly include adversarial training, defensive distillation, and input transformation. These methods can improve model robustness to a certain extent, but they have the following drawbacks: significantly increased training time, potential reduction in accuracy on normal samples, limited effectiveness against unseen attacks, lack of targeted optimization measures, and difficulty in quantifying the improvement effect on specific weaknesses. Summary of the Invention

[0006] The purpose of this invention is to provide a method for intelligent algorithm weakness discovery and iterative optimization based on interpretable analysis, overcoming the problems of separation between interpretability and weakness discovery, lack of systematic localization methods, low testing efficiency, and lack of targeted optimization in the existing technology.

[0007] Specifically, this invention aims to achieve the following technical objectives: to automatically locate weak links in algorithms based on interpretability analysis, to evaluate the robustness of components through multi-dimensional systematic testing, to efficiently identify key weaknesses affecting algorithm performance, to establish a complete closed loop from weakness discovery to targeted optimization, and to support a general method for various types of intelligent algorithms.

[0008] This invention provides a method for discovering and iteratively optimizing the weaknesses of intelligent algorithms based on interpretable analysis. This method organically combines interpretability analysis with weakness discovery testing to achieve accurate positioning and targeted optimization of the weak links of intelligent algorithms.

[0009] The method of this invention includes the following steps: First, an interpretable analysis engine is used to analyze the internal mechanism of the intelligent algorithm under test to obtain information on key components; second, a focus extraction module is used to automatically identify the core components affecting the algorithm's decision-making and form a component list; then, a targeted adjustment system is used to conduct multi-dimensional perturbation tests on the identified key components; simultaneously, a change observation mechanism is used to monitor the refined changes in the algorithm's performance before and after the perturbation; furthermore, a pattern comparison analyzer is used to analyze weakness patterns across samples to locate the most critical weak link; finally, a targeted optimization strategy is generated based on the weakness analysis results for iterative improvement.

[0010] The core algorithmic innovation of this invention includes three key points:

[0011] (1) Multi-dimensional combination search algorithm for five-dimensional perturbation parameter space

[0012] Unlike existing technologies that only test in a single dimension (such as L∞ norm perturbation), this invention proposes for the first time to perform a systematic search in a five-dimensional perturbation parameter space P = (component_type, num_components, noise_type, noise_level, target_confidence). The five dimensions are as follows: component_type specifies which type of component (e.g., small target, occluded target) to perturb; num_components specifies the number of components to perturb simultaneously; noise_type specifies the perturbation method (e.g., blurring, occlusion, noise); noise_level specifies the perturbation intensity; and target_confidence specifies the desired change in confidence level.

[0013] The specific process of the multi-dimensional combined search algorithm is as follows:

[0014] In the first step, for each type of key component (selected from the top-K components identified by interpretability analysis), different numbers of perturbations (such as perturbing 1, 2, or 3 components simultaneously) and perturbation types (such as blurring, occlusion, noise, etc.) are traversed to form a three-dimensional combination space.

[0015] In the second step, for each three-dimensional combination (component_type, num_components, noise_type), the binary search algorithm is used to search for the minimum effective perturbation intensity within the perturbation intensity range [εmin, εmax]: the midpoint εmid is taken for testing. If the average confidence drops to reach the target threshold target_confidence, then this intensity is recorded and the search interval is narrowed to [εmin, εmid]; otherwise, it is expanded to [εmid, ε_max], and the iteration continues until convergence.

[0016] In the third step, the influence effect of each perturbation combination is evaluated on the test sample set. If the success rate (the proportion of samples resulting in performance degradation) exceeds the preset threshold θ_effective, it is recorded as a candidate weakness pattern.

[0017] In the fourth step, the candidate weaknesses are sorted according to the influence effect, and the top-M most effective single perturbations are selected.

[0018] In the fifth step, the selected effective perturbations are tested in pairs. If the influence effect of the combined perturbation is significantly greater than the sum of the effects of each individual perturbation (i.e., there is a synergistic effect), it is identified as a composite weakness pattern.

[0019] Through the above five-step process, the algorithm can systematically search in a five-dimensional parameter space, which not only ensures the comprehensiveness of the test (covering various component types, perturbation methods, and intensity combinations), but also ensures the efficiency through binary search and top-M screening, and can discover composite weaknesses that cannot be identified by traditional single-dimensional tests.

[0020] (2) Directed testing strategy based on interpretability analysis

[0021] Traditional weakness discovery methods use random testing, which is inefficient. The present invention innovatively transforms interpretability analysis (Grad-CAM, attention weights, etc.) from a post hoc interpretation tool into a pre-test guidance tool. By extracting and clustering and analyzing component importance information, the top-K key components are identified, and the test focus is concentrated on the components that are truly important for algorithm decision-making, greatly reducing the test space (from O(N×M) to O(K×M), where K << N), while improving the accuracy of weakness discovery.

[0022] (3) Cross-sample weakness pattern recognition and severity quantification

[0023] This invention designs a cross-sample statistical analysis method to extract systemic weakness patterns from individual test results. A frequency mining algorithm identifies weaknesses whose frequency exceeds the minimum support threshold, and these weaknesses are quantified and ranked using the severity scoring formula Severity(pattern) = w1·frequency + w2·avg_impact + w3·(1-normalized_variance), effectively filtering out accidental failures and identifying true systemic weaknesses.

[0024] The three core algorithms mentioned above together constitute the complete technical system of this invention, from efficient testing to accurate positioning and then to targeted optimization. This is the key innovation that distinguishes this invention from existing technologies.

[0025] The system of this invention consists of the following five core components:

[0026] The interpretable analysis engine is responsible for analyzing the internal mechanism of the algorithm and extracting the importance information of the components; the attention extraction module performs cluster analysis on the extracted components and identifies key components; the targeted adjustment system performs a systematic search in the five-dimensional perturbation parameter space and applies perturbations to key components; the change observation mechanism monitors the multi-dimensional performance changes before and after the perturbation; the pattern comparison analyzer identifies weakness patterns and calculates severity scores through cross-sample statistical analysis.

[0027] The five core components form a complete vulnerability discovery process (see Figure 2 Vulnerability Discovery Flowchart): from component extraction by the interpretable analysis engine, to key component identification by the concern extraction module, to targeted adjustment of the system's perturbation testing and performance monitoring of the change observation mechanism, and finally to identification of systemic vulnerability patterns by the pattern comparison analyzer.

[0028] Based on the vulnerability discovery results, the system generates targeted optimization strategies and iteratively improves them (see Figure 3 Iterative Optimization Flowchart), including measures such as data augmentation, loss function adjustment, and model architecture optimization, forming a complete closed loop from vulnerability discovery to optimization.

[0029] Detailed description of core components:

[0030] (1) The interpretable analysis engine is used to analyze the internal decision-making mechanism of intelligent algorithms and extract the importance information of key features and components.

[0031] In terms of feature importance calculation, for an input sample x, the importance score is determined by calculating the gradient of each component c_i with respect to the model output f(x), i.e. Where I(c_i) represents the component importance score.

[0032] In terms of multi-level analysis, a hierarchical interpretable approach is adopted for computer vision algorithms: for image data, a semantic segmentation model is used to decompose the image into semantic components, calculate the contribution of each component to object detection or classification, and support multi-granularity analysis at the pixel level, region level, and object level; the importance of components is calculated through gradient visualization methods such as Grad-CAM, key regions are identified by gradient backpropagation, and feature contributions are analyzed by combining activation maps.

[0033] The component is represented by a quadruple, namely C = (type, region, confidence, importance), where type represents the component category, region represents the spatial location or bounding box coordinates of the component region, confidence represents the algorithm's confidence in the component, and importance represents the importance score of the component.

[0034] (2) The focus extraction module is used to automatically filter out the most critical components for algorithm decision-making from a large number of components.

[0035] This module first performs cross-sample clustering on the components of multiple samples. By calculating the similarity between components, similar components are grouped into the same cluster to obtain common component categories. These common components represent patterns that the algorithm generally relies on in this task.

[0036] Then, a comprehensive importance score S(Cluster_j) is calculated for each cluster. This score takes into account the average importance of the components in the cluster, the frequency of occurrence of the cluster, and the average confidence. The calculation formula is: S(Cluster_j)=α·Avg(importance)+β·Freq(Cluster_j)+γ·Avg(confidence), where α, β, and γ are weight coefficients and satisfy α+β+γ=1.

[0037] Finally, based on the overall importance score, the top-K highest-scoring component clusters were selected as key components for subsequent targeted testing.

[0038] (3) The targeted adjustment system is used to perform multi-dimensional perturbation on key components and systematically test the robustness of the algorithm.

[0039] One of the key innovations of this invention is the definition of a five-dimensional perturbation parameter space P = (component_type, num_components, noise_type, noise_level, target_confidence), where component_type indicates which type of component is selected for perturbation, num_components indicates the number of components perturbed simultaneously, noise_type indicates the perturbation type, noise_level indicates the perturbation intensity, and target_confidence indicates the target confidence range.

[0040] Perturbation methods for image components include, but are not limited to: occlusion perturbation, blur perturbation, brightness adjustment, contrast adjustment, noise injection, geometric deformation, and color perturbation. Occlusion perturbation is achieved by filling the component area with a specific color or background; blur perturbation is achieved by applying Gaussian blur to the component area; and geometric deformation includes scaling, rotation, and perspective transformation.

[0041] Another key innovation of this invention lies in the multi-dimensional combination search method, which uses an adaptive sampling strategy to systematically search for the most effective combination of perturbations in the five-dimensional perturbation parameter space (see the perturbation testing step in the weakness discovery flowchart in Figure 2 for details):

[0042] The core steps of the multi-dimensional combination search method include: First, for each key component type, each number of perturbations, and each combination of perturbation types, a binary search algorithm is used to search for the minimum effective perturbation intensity that can achieve a change in target confidence within the perturbation intensity range; Second, the impact of the perturbation combination is evaluated on the test sample set, and if the impact exceeds a preset threshold, it is recorded as a candidate weakness pattern; Then, the candidate perturbation combinations are sorted according to their impact, and the Top-M most effective perturbations are selected; Finally, the selected effective perturbations are tested in pairs, and if the impact of the combined perturbation is significantly greater than the sum of the effects of each individual perturbation, it is identified as a composite weakness pattern.

[0043] The binary search algorithm iteratively searches within the perturbation intensity range [εmin, εmax], taking the midpoint for testing each time and adjusting the search range based on the test results until the minimum effective perturbation intensity is found. Compared with the traditional linear search method, this algorithm can significantly improve search efficiency.

[0044] (4) The change observation mechanism is used to finely monitor the changes in algorithm performance before and after the disturbance.

[0045] This mechanism monitors multi-dimensional performance metrics, including changes in confidence (Δconfidence = confidence_after-confidence_before), whether the prediction results have changed, changes in Top-K prediction results, changes in intermediate layer activation, and distance to the decision boundary.

[0046] For each component category, the relationship curve between disturbance intensity and confidence level is recorded. By analyzing the performance change trend under different disturbance intensities, the vulnerability characteristics of the component are determined.

[0047] The sensitivity of a component to disturbances is quantified by calculating Sensitivity(c_i) = dΔconfidence / dε, where ε represents the disturbance intensity. Highly sensitive components are identified as weak points in the algorithm.

[0048] (5) The pattern comparison analyzer is used to analyze weakness patterns across samples and identify systemic weaknesses.

[0049] The weakness pattern is represented in a structured manner, including information such as component feature description, effective perturbation parameters, frequency of occurrence in the test set, average impact, impact variance, and a list of affected samples.

[0050] The pattern comparison analyzer identifies frequently occurring weakness patterns using a pattern mining algorithm. The specific steps are as follows: First, extract valid perturbations whose influence exceeds the minimum influence threshold from all perturbation test results; second, group the valid perturbations according to component characteristics; then, calculate the frequency of each group in the test set. If the frequency exceeds the minimum support threshold, calculate the average influence and influence variance of that group and create a corresponding weakness pattern; finally, sort the weakness patterns according to the product of frequency and average influence.

[0051] The severity of weaknesses is quantified by the comprehensive scoring formula Severity(pattern)=w1·frequency+w2·avg_impact+w3·(1-normalized_variance), where frequency represents the scope of influence, avg_impact represents the degree of influence, the reciprocal of normalized_variance represents stability, and w1, w2, and w3 are weighting coefficients.

[0052] Based on the identified weakness patterns, this invention generates targeted optimization strategies and iteratively improves them.

[0053] The targeted optimization strategy includes three aspects: data augmentation, loss function adjustment, and model architecture adjustment. For data augmentation, adversarial examples are generated for the identified weak components and added to the training set to form an augmented dataset. For loss function adjustment, a robustness regularization term targeting the weak components is added to the original loss function. The adjusted total loss function is L_total=L_original+λ·∑_{pattern}w(pattern)·L_robust(pattern), where w(pattern) is proportional to the severity of the weakness, and λ is the weight coefficient of the robustness loss. For model architecture adjustment, dedicated processing branches are added to the weak components, the weight allocation of the attention mechanism is adjusted, and the ability to extract relevant features is enhanced.

[0054] After optimization, the vulnerability discovery process is re-executed, and the improvement effect is evaluated by comparing the vulnerability severity scores before and after optimization. If serious weaknesses still exist, iterative optimization continues until the weaknesses are significantly improved.

[0055] Compared with existing technologies, this invention establishes a complete technical chain from understanding internal mechanisms to locating weaknesses by organically combining interpretability analysis with vulnerability discovery testing, and has the following significant technical advantages and beneficial effects:

[0056] This invention, based on an interpretability-guided targeted testing mechanism, achieves higher accuracy in weakness detection compared to traditional random testing methods. In terms of technical principles, this invention employs interpretability analysis methods such as Grad-CAM and attention weights to accurately identify key components, focusing testing on components truly important to algorithmic decisions and avoiding the ineffectiveness of traditional blind random testing. Furthermore, this invention performs a systematic search within a five-dimensional perturbation parameter space (component category, quantity, perturbation type, intensity, and target confidence), which, compared to traditional methods considering only a single perturbation dimension (such as L∞ norm perturbation), provides a more comprehensive coverage of the potential weakness space. This invention identifies systemic weaknesses through cross-sample frequency analysis and sets a minimum support threshold to filter out accidental failures, effectively reducing the false positive rate and improving the accuracy of weakness identification. In various application scenarios, this invention's method effectively identifies typical weaknesses in algorithms. For example, in target detection algorithms, it accurately identifies weaknesses such as insufficient detection of small targets and performance degradation in occluded scenes; in image classification algorithms, it identifies problems such as sensitivity to background noise and vulnerability to adversarial perturbations; and in semantic segmentation algorithms, it discovers defects such as blurred boundaries and category confusion.

[0057] This invention overcomes the limitations of traditional sample-level coarse-grained analysis, achieving fine-grained weakness localization at the component level. Technically, this invention uses a semantic segmentation model to decompose images into semantic components, refining weakness localization from "the entire sample fails" to "specific component is vulnerable." By applying perturbations to each component individually and monitoring performance changes, it can accurately identify which component categories perform poorly under what perturbation conditions. This invention uses a quadruple C = (type, region, confidence, importance) to fully describe weakness features, providing a clear direction for targeted optimization. In practical applications, this invention can provide accurate weakness localization information. For example, in target detection scenarios, it can locate specific target size ranges and perturbation type combinations; in image classification scenarios, it can identify the sensitivity of specific background components to specific perturbations; and in semantic segmentation scenarios, it can locate the vulnerability of boundary regions to noise. This fine-grained localization provides clear targets for subsequent optimization.

[0058] This invention significantly improves testing efficiency compared to traditional black-box testing methods through targeted testing and intelligent search strategies. The efficiency improvement is reflected in several aspects: First, this invention uses interpretability analysis to identify Top-K key components before targeted testing, avoiding the blindness of traditional methods that randomly generate a large number of test samples, thus reducing the test space from testing the entire set to testing key components. Second, this invention uses a binary search algorithm to determine the minimum effective perturbation strength, which significantly reduces the number of iterations compared to traditional methods (such as PGD) that require tens to hundreds of iterations to gradually approximate the minimum perturbation strength. Third, this invention identifies weakness patterns from a limited number of samples and generalizes them to similar samples through cross-sample pattern induction, avoiding the redundancy of traditional methods that test each sample independently. These technical features work together to ensure the accuracy of weakness discovery while significantly reducing the number of test samples and testing time, especially in large-scale models and massive data scenarios, where the efficiency advantage is even more significant.

[0059] This invention, based on precise weakness localization information, can generate targeted optimization strategies, achieving a more significant improvement compared to traditional general optimization methods. The improved optimization effect stems from the following technical advantages: First, this invention can accurately locate the specific component categories and perturbation types requiring optimization, allowing optimization resources to be concentrated on the truly weak points, avoiding resource dispersion caused by blindly adding adversarial examples or global retraining in traditional methods. Second, this invention generates adversarial examples based on identified weakness patterns, which are more targeted and effective than randomly generated adversarial examples, improving training data quality and accelerating convergence. Third, this invention employs a hierarchical optimization strategy, setting different optimization weights for weaknesses of varying severity, highlighting the key areas for model optimization and focusing on improving weak points. In practical applications, this invention's method can achieve targeted performance improvements in various computer vision tasks such as object detection, image classification, and semantic segmentation, particularly showing significant performance improvements in weak component optimization, demonstrating a clear advantage over traditional general optimization methods.

[0060] This invention's method exhibits excellent versatility in the field of computer vision, applicable to various visual tasks such as object detection, image classification, and semantic segmentation. This versatility stems from the modular design of this invention: the core process of weakness discovery (component extraction → perturbation testing → pattern recognition → optimization) is independent of specific visual tasks; adaptation is achieved simply by configuring the corresponding component definitions and evaluation metrics for different tasks. The interpretability analysis engine supports configuring different analysis methods for different tasks. For example, Grad-CAM and region importance analysis can be configured for object detection tasks, class activation maps and saliency maps for image classification tasks, and pixel-level importance and boundary sensitivity analysis for semantic segmentation tasks. The image perturbation strategy has good scalability, supporting various perturbation methods such as occlusion, blurring, noise, and geometric transformation, and new perturbation types can be added according to specific needs. This invention's method is applicable to deep learning-based computer vision algorithms, including but not limited to convolutional neural networks and Transformer architectures, and can effectively discover and optimize algorithm weaknesses in various tasks such as object detection, image classification, semantic segmentation, and instance segmentation.

[0061] The beneficial effects of this invention are as follows:

[0062] 1. This invention is the first to organically combine interpretability analysis with vulnerability discovery, establishing a complete technical link from understanding internal mechanisms to locating weak links, breaking through the limitation of the separation of the two in the prior art;

[0063] 2. This invention proposes a multi-dimensional composite weakness search algorithm, which systematically explores the combination of dimensions such as component category, quantity, perturbation type, intensity, and target confidence in a five-dimensional perturbation parameter space, and has a more comprehensive coverage capability compared with traditional single-dimensional testing methods;

[0064] 3. This invention achieves fine-grained weakness localization at the component level. Through semantic component decomposition and independent testing mechanisms, it breaks through the limitations of coarse-grained analysis at the sample level in traditional methods, providing precise targets for targeted optimization.

[0065] 4. This invention constructs a complete closed-loop mechanism from weakness discovery to targeted optimization, including weakness identification, optimization strategy generation, effect evaluation, iterative improvement, etc., realizing an efficient iterative optimization process;

[0066] 5. This invention designs a cross-sample weakness pattern recognition method, which extracts systematic patterns from individual failure cases through statistical analysis and identifies universal weakness patterns, avoiding the problem of traditional methods misjudging random failures as systematic weaknesses. Attached Figure Description

[0067] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0068] Figure 1 shows the system architecture of the present invention;

[0069] Figure 2 is a flowchart of the vulnerability discovery process of this invention;

[0070] Figure 3 is a flowchart of the iterative optimization process of this invention. Detailed Implementation

[0071] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0072] Figure 1 shows the system architecture of the present invention, which includes: an input layer, a core processing layer, an iterative optimization layer, and an output layer.

[0073] Specifically, the input layer includes receiving the algorithm to be tested, test data, annotations (optional), and configuration parameters.

[0074] The core processing layer is divided into a first stage and a second stage. The first stage explains the internal mechanism of the analysis engine's algorithm, while the focus extraction module identifies key components.

[0075] Phase Two: Targeted adjustments are made to the system to conduct multi-dimensional perturbation tests; a change observation mechanism monitors performance changes; a pattern comparison analyzer identifies weakness patterns; and the three work together to form a closely coordinated analysis loop.

[0076] Figure 2 shows the flowchart for the vulnerability discovery process of this invention, and the steps are as follows:

[0077] 1) Input processing: Loading test samples and algorithms;

[0078] 2) Interpretability analysis: Extracting component importance information;

[0079] 3) Key component identification: Screening Top-K key components;

[0080] 4) Initialize disturbance parameters: Set the five-dimensional disturbance space;

[0081] 5) Multi-dimensional disturbance testing: Systematically testing various combinations;

[0082] 6) Performance monitoring: Record changes in confidence level, prediction, etc.

[0083] 7) Sensitivity Calculation: Analyze the sensitivity of each component;

[0084] 8) Pattern recognition: Identifying patterns of weaknesses across samples;

[0085] 9) Severity score: Assess the severity of the weakness;

[0086] 10) Report generation: Outputs a detailed vulnerability analysis report.

[0087] Figure 3 shows the iterative optimization flowchart of this invention, and the steps are as follows:

[0088] 1. Weakness Input: Read the weakness analysis results;

[0089] 2. Strategy Generation: Generate optimization strategies based on the type of weakness;

[0090] 3. Data Augmentation: Generate targeted adversarial examples;

[0091] 4. Loss Adjustment: Add regularization to weak components;

[0092] 5. Model Training: Retrain / fine-tuning using augmented data;

[0093] 6. Effectiveness Evaluation: Evaluate the improvement effect on the test set;

[0094] 7. Iterative judgment:

[0095] 1) If the weaknesses are significantly improved and no new weaknesses are found, output the optimized model;

[0096] 2) If weaknesses still exist, return to step 1 to continue optimization;

[0097] 8. Final output: Optimized model and performance report.

[0098] The following two examples illustrate this in detail.

[0099] Example 1: Weakness discovery and optimization of YOLO target detection algorithm.

[0100] This embodiment uses the YOLOv5 object detection algorithm in a vehicle detection task in an autonomous driving scenario as an example to illustrate the specific implementation process of the method of the present invention.

[0101] Algorithm configuration: Model: YOLOv5s; Number of parameters: 7.2M; Input size: 640×640; Training dataset: KITTI vehicle detection dataset; Baseline performance: mAP@0.5 is 85% (example value).

[0102] Implementation objective: To use the method of this invention to identify the weaknesses of the algorithm in complex scenarios and to perform targeted optimization.

[0103] The steps are as follows: Step 1: Interpretability analysis; Step 2: Focus extraction; Step 3: Targeted perturbation testing; Step 4: Change observation; Step 5: Weakness pattern recognition; Step 6: Targeted optimization.

[0104] Specifically, Step 1: Interpretability analysis includes the following steps:

[0105] (1) Image segmentation

[0106] Use a semantic segmentation model (such as DeepLabV3+) to segment the test image and identify the various semantic components in the image:

[0107]

[0108]

[0109] In this embodiment, a typical image is segmented into 15-30 components, including: vehicles: vehicles of different sizes and positions (5-10); roads: road surface areas (1-2); buildings: background buildings (3-5); vegetation: trees and grass (2-4); sky: sky area (1); other: traffic signs, pedestrians, etc. (2-5).

[0110] (2) YOLO detection and component association

[0111] For each segmented component, run YOLO detection and calculate the confidence score:

[0112]

[0113]

[0114] (3) Component Importance Calculation

[0115] Grad-CAM is used to calculate the importance of each component to the detection results:

[0116] defcompute_importance(component,image,yolo_model):

[0117] #Importance of using Grad-CAM to calculate component regions

[0118] gradcam=GradCAM(yolo_model,target_layer='backbone.layer4')

[0119] #Get the activation map of the component area

[0120] activation_map=gradcam.generate(image)

[0121] #Calculate the average activation intensity within the component region

[0122] mask = component['mask']

[0123] importance=mean(activation_map[mask])

[0124] component['importance']=importance

[0125] return component

[0126] Analysis results:

[0127] In this embodiment, component extraction was performed on 100 test images, and the typical results are as follows: vehicle components: approximately 680 (average importance: 0.78); road components: approximately 120 (average importance: 0.45); background components: approximately 1350 (average importance: 0.23).

[0128] Note: Specific values ​​may vary depending on the actual image content and the performance of the segmentation model.

[0129] Specifically, step 2: Focus Extraction includes the following steps:

[0130] (1) Component clustering; clustering is performed according to component category, size, and location:

[0131]

[0132]

[0133] Clustering results:

[0134] This embodiment identified 15 component clusters, and the top-5 component categories, ranked by importance, are as follows:

[0135] 1. Large vehicles (close range): Occurred approximately 90% of the time, with an average confidence level of approximately 0.92.

[0136] 2. Medium-sized vehicles (medium distance): Occurred approximately 75% of the time, with an average confidence level of approximately 0.81.

[0137] 3. Small vehicles (at a distance): Occurrence frequency approximately 45%, average confidence level approximately 0.58 (potential weakness) 4. Partially obscured vehicles: Occurrence frequency approximately 30%, average confidence level approximately 0.65 (potential weakness)

[0138] 5. Road area: Occurred approximately 95% of the time, with an average confidence level of approximately 0.88 (affecting contextual understanding) (2) Key component selection; Calculate the overall importance score and select Top-K:

[0139]

[0140]

[0141] Selection result: The above Top-5 clusters are selected as the key components.

[0142] Specifically, step 3: targeted perturbation testing includes the following steps:

[0143] (1) Define the five-dimensional perturbation space

[0144]

[0145] (2) Component-level disturbance generation

[0146] For fuzzy perturbations in "small vehicle" components:

[0147]

[0148]

[0149] (3) Multi-dimensional combined search: Execute a multi-dimensional weakness search algorithm.

[0150]

[0151]

[0152]

[0153] Binary search implementation:

[0154]

[0155]

[0156] Test results:

[0157] In this embodiment, the following weakness patterns were identified using a multi-dimensional weakness search algorithm on 100 test images, as shown in the table below:

[0158]

[0159] Note: Specific values ​​may vary depending on model characteristics, data distribution, and other factors. Composite Weakness Test:

[0160] Test the combination of "small vehicle + blur" and "road area + reduced brightness":

[0161] Two composite vulnerability patterns were identified.

[0162] Specifically, step 4: Change observation includes the following steps:

[0163] Record detailed performance changes:

[0164]

[0165]

[0166] Observation results:

[0167] For the weak mode "small vehicle + fuzz (intensity 0.35)", the following performance changes were observed:

[0168] Average confidence level: 0.58 → 0.30 (a decrease of approximately 48%); false negative rate: 0% → approximately 23%; detection frame IoU: approximately 0.85 (compared to before the perturbation); changes in Layer 4 activation: average L2 distance increased by approximately 65%.

[0169] Note: The above data is based on the test results of this embodiment.

[0170] Specifically, step 5: Weakness pattern recognition includes the following steps:

[0171] Cross-sample statistical analysis:

[0172]

[0173]

[0174] Top-3 identified weakness patterns: 1. Small vehicle + blurry perturbation: Frequency: 0.78; Average impact: 0.28; Severity score: 0.189. 2. Small vehicle + noise perturbation (composite): Frequency: 0.85; Average impact: 0.32; Severity score: 0.242. 3. Occluded vehicle + occlusion enhancement: Frequency: 0.81; Average impact: 0.35; Severity score: 0.251.

[0175] Specifically, step 6: Change observation includes the following steps:

[0176] (1) Data Augmentation Strategy

[0177] Generate adversarial examples based on identified weaknesses:

[0178]

[0179]

[0180] After generating adversarial examples, the training set was expanded from 5,000 to 8,000 (an increase of 60%).

[0181] (2) Loss function adjustment

[0182] Increase robustness regularization for vulnerable components:

[0183]

[0184]

[0185] (3) Model fine-tuning

[0186] Fine-tuning the model on augmented datasets:

[0187] Training configuration:

[0188] Learning rate: 0.001 (initial) → 0.0001 (reduced by a factor of 10)

[0189] -Epochs:20 (fine-tuning)

[0190] -Batch size:16

[0191] -Optimizer: Adam

[0192] - Loss function: Standard YOLO loss + robust regularization

[0193] (4) Effect evaluation

[0194] On the test set of this embodiment, the optimized model performance is as follows:

[0195]

[0196] Key improvements:

[0197] • Significantly improved performance of weak components: Performance of weak components in small vehicles and vehicles with obstructed views is improved by 13-15 percentage points.

[0198] • Improved robustness against adversarial attacks: Performance is improved by 12-15 percentage points in adversarial scenarios involving blur and noise.

[0199] • High training efficiency: Compared to full retraining, fine-tuning can save approximately 75% of training time. Note: Specific results may vary depending on factors such as model structure, dataset characteristics, and optimization parameters.

[0200] Summary of Implementation Results

[0201] This embodiment demonstrates the complete process of the method of the present invention in the discovery and optimization of weaknesses in target detection algorithms:

[0202] 1. Precise positioning capability: Through interpretability analysis and multi-dimensional testing, it accurately locates weak links at the component level, such as "small-sized vehicle inspection".

[0203] 2. Efficient Testing: Compared to traditional random testing which requires a large number of samples, this method only needs a small number of samples to identify key weaknesses.

[0204] 3. Significant optimization results: Targeted optimization strategies improve the performance of weak components by 13-15 percentage points, which is significantly better than general optimization methods.

[0205] 4. Time efficiency: Fine-tuning can save approximately 75% of training time, significantly improving optimization efficiency.

[0206] This embodiment demonstrates the invention's features: interpretable guidance, precise positioning, efficient testing, and targeted optimization.

[0207] Example 2: Discovery of weaknesses in the ResNet image classification algorithm.

[0208] This embodiment uses the discovery of weaknesses in the ResNet-50 model in the ImageNet image classification task as an example to demonstrate the application of the method of the present invention in image classification scenarios.

[0209] Algorithm configuration: Model: ResNet-50; Number of parameters: 25.6M; Task: 1000-class image classification; Training dataset: ImageNet dataset; Baseline performance: Top-1 accuracy 76%, Top-5 accuracy 93% (example values).

[0210] Objective: To discover the model's vulnerability to background noise and adversarial perturbations.

[0211] The implementation process of this embodiment is similar to that of embodiment 1, with the key difference being:

[0212] Step 1: Component Extraction: Use GradCAM to identify the regions in the image that are most important for classification decisions; decompose the image into components such as foreground objects, background textures, and color regions.

[0213] Steps 2-3: Focus Identification and Perturbation Testing: Key weaknesses were identified through multi-dimensional testing: over-reliance on classification for background texture; sensitivity to local noise; and vulnerability to color perturbations.

[0214] Weaknesses in identification:

[0215] 1. Background texture dependency: Adding noise to the background area increases the classification error rate by approximately 30%.

[0216] 2. Local adversarial perturbations: Small-area adversarial perturbations (ε<0.05) cause a decrease in Top-1 accuracy of approximately 18%.

[0217] 3. Color shift: When the hue shift is greater than 30 degrees, the classification accuracy decreases by approximately 12%.

[0218] Steps 4-6: Optimization and Validation: Data augmentation and robustness training based on identified weaknesses; Optimization results: Adversarial robustness improved by approximately 40%; Background noise robustness improved by approximately 25%; Overall Top-1 accuracy improved by 1.5pp.

[0219] This embodiment verifies the effectiveness of the method of the present invention in image classification tasks, demonstrating that the same framework can be applied to different computer vision tasks.

[0220] The two embodiments above cover two typical tasks in the field of computer vision: object detection and image classification, fully demonstrating the method of the present invention:

[0221] 1. Technical effectiveness: It can accurately identify the weaknesses of the algorithm, and the optimization effect is significantly better than traditional methods.

[0222] 2. High efficiency: Targeted testing can reduce the number of test samples, and fine-tuning and optimization can save training time.

[0223] 3. Interpretable Results: Provides a complete and interpretable analysis chain from component identification and vulnerability localization to optimization suggestions.

[0224] 4. Wide applicability: By configuring different component definitions and perturbation methods, it can be applied to various computer vision tasks such as object detection, image classification, and semantic segmentation.

[0225] The core value of this invention lies in its organic combination of interpretability analysis and vulnerability discovery, achieving a complete technical loop from "discovering problems" to "solving problems" for computer vision algorithms.

[0226] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for intelligent algorithm weakness discovery and iterative optimization based on interpretable analysis, characterized in that, The process includes the following steps: Step S1: Use an interpretable analysis engine to analyze the internal mechanism of the intelligent algorithm under test. For each sample in the test sample set, extract the component information that affects the algorithm's decision. The component information includes component category, component region, algorithm confidence in the component, and component importance score; Step S2: Perform cross-sample clustering analysis on the component information extracted in Step S1 through the attention extraction module, calculate the comprehensive importance score of each component cluster, and select the Top-K component clusters with the highest comprehensive importance scores as key components; Step S3: For the key components identified in Step S2, a targeted adjustment system is used to systematically search in a five-dimensional perturbation parameter space. This space includes dimensions for component category, number of perturbation components, perturbation type, perturbation intensity, and target confidence change. For each perturbation parameter combination, a binary search algorithm is used to determine the minimum effective perturbation intensity required to achieve the target confidence change, and the impact of this perturbation combination is evaluated on a test sample set. Step S4: A change observation mechanism is used to monitor the performance changes of the algorithm before and after the perturbation in Step S3. Multi-dimensional performance indicators, including changes in confidence, prediction results, detection boxes, and intermediate layer activation, are recorded, and the sensitivity of each component category to the perturbation is calculated. Step S5: A pattern comparison analyzer is used to perform cross-sample statistical analysis on the performance change data collected in Step S4 to identify systematic weakness patterns. These patterns include component feature descriptions, effective perturbation parameters, frequency of occurrence, average impact degree, and impact variance. A severity score for each weakness pattern is calculated. Step S6: Generate targeted optimization strategies based on the weakness patterns identified in Step S5, including generating adversarial examples for weak components to augment data, adding robustness regularization terms for weak components to the loss function, and adjusting the model architecture to enhance the ability to handle weak components. Use the optimization strategies to iteratively improve the algorithm, and re-execute Steps S1 to S5 to evaluate the optimization effect until the weaknesses are significantly improved.

2. The method for intelligent algorithm weakness discovery and iterative optimization based on interpretable analysis according to claim 1, characterized in that, The interpretable analysis engine in step S1 employs different analysis methods for different types of computer vision algorithms: for object detection algorithms, a semantic segmentation model is used to decompose the image into semantic components, and Grad-CAM or class activation mapping is used to calculate the importance score of each component; for image classification algorithms, feature maps of convolutional layers are extracted, and Grad-CAM or integral gradient methods are used to calculate the importance score of image regions; for semantic segmentation algorithms, each segmented region is identified as a component, and attention mechanisms or SHAP values ​​are used to calculate the importance of the components.

3. The method for intelligent algorithm weakness discovery and iterative optimization based on interpretable analysis according to claim 1, characterized in that, The component in step S1 is represented as a quadruple: C = (type, region, confidence, importance), where type represents the component category, including object category, part-of-speech category, and feature category; region represents the spatial or location information of the component; confidence represents the algorithm's confidence in the component; and importance represents the importance score of the component to the algorithm's decision.

4. The method for intelligent algorithm weakness discovery and iterative optimization based on interpretable analysis according to claim 1, characterized in that, The formula for calculating the comprehensive importance score of the component cluster in step S2 is: S(Cluster_j)=α·Avg(importance)+β·Freq(Cluster_j)+γ·Avg(confidence); where Avg(importance) represents the average importance score of the components in the cluster, Freq(Cluster_j) represents the frequency of occurrence of the cluster in the test sample set, Avg(confidence) represents the average confidence of the components in the cluster, and α, β, γ are weight coefficients that satisfy α+β+γ=1 and α, β, γ∈(0,1).

5. The method for intelligent algorithm weakness discovery and iterative optimization based on interpretable analysis according to claim 1, characterized in that, The specific steps of the binary search algorithm in step S3 are as follows: (1) Initialize the perturbation strength search range to [εmin, εmax]; (2) Generate a perturbation sample at the midpoint ε_mid of the current range, and evaluate the average confidence change Δconf on the sampled test sample; (3) If Δconf is greater than or equal to the target confidence change, record εmid as the effective perturbation strength and update the search range to [εmin, εmid]; otherwise, update the search range to [εmid, ε_max]; (4) Repeat steps (2) and (3) until the search range converges or the maximum number of iterations is reached, and return the minimum effective perturbation strength found.

6. The method for intelligent algorithm weakness discovery and iterative optimization based on interpretable analysis according to claim 1, characterized in that, The perturbation types for image data in step S3 include at least one of the following: component region occlusion, Gaussian blur, brightness change, contrast adjustment, noise addition, and geometric transformation, wherein geometric transformation includes rotation, scaling, translation, and affine transformation.

7. The method for intelligent algorithm weakness discovery and iterative optimization based on interpretable analysis according to claim 1, characterized in that, Step S3 further includes the step of identifying composite weaknesses: for the identified multiple effective perturbation combinations, pairwise combination tests are performed. If the effect of the combined perturbation is significantly greater than the sum of the effects of each individual perturbation, then the combination is marked as a composite weakness pattern.

8. The method for intelligent algorithm weakness discovery and iterative optimization based on interpretable analysis according to claim 1, characterized in that, The formula for calculating component sensitivity in S4 is: Sensitivity(c_i)=dΔconfidence / dε|_{ε=ε_0}; where Δconfidence represents the confidence change, ε represents the perturbation intensity, and ε_0 represents a specific perturbation intensity point. High-sensitivity components are identified as weak links in the algorithm.

9. The method for intelligent algorithm weakness discovery and iterative optimization based on interpretable analysis according to claim 1, characterized in that, The formula for calculating the severity score of the weakness pattern in step S5 is: Severity(pattern) = w1·frequency + w2·avg_impact + w3·(1-normalized_variance), where frequency represents the frequency of occurrence of the weakness pattern in the test set; avg_impact represents the average impact of the weakness pattern; normalized_variance represents the normalized variance of the impact; and w1, w2, and w3 are weighting coefficients.

10. The method for intelligent algorithm weakness discovery and iterative optimization based on interpretable analysis according to claim 1, characterized in that, The formula for adjusting the loss function in step S6 is: L_total=L_original+λ·∑_{pattern}w(pattern)·L_robust(pattern); where L_original represents the original loss function; L_robust(pattern) represents the robustness loss for a specific weakness pattern; w(pattern) represents the weight of the weakness pattern, which is proportional to its severity score; and λ is the overall weight coefficient of the robustness loss.

11. A system for intelligent algorithm weakness discovery and iterative optimization based on interpretable analysis, characterized in that, include: An interpretable analytics engine is used to analyze the internal mechanisms of the intelligent algorithm under test and extract information about the components that affect the algorithm's decisions. The attention extraction module, connected to the interpretable analysis engine, is used to perform cross-sample clustering analysis on the extracted component information to identify key components. The targeted adjustment system, connected to the attention extraction module, is used to perform a systematic search in the five-dimensional perturbation parameter space to generate targeted perturbations and evaluate their impact. The change observation mechanism, connected to the targeted adjustment system, is used to monitor multi-dimensional performance changes of the algorithm before and after perturbation and calculate component sensitivity. The pattern comparison analyzer, connected to the change observation mechanism, is used to perform cross-sample statistical analysis to identify systemic weakness patterns and calculate severity scores. An optimization strategy generation module, connected to the pattern comparison analyzer, is used to generate targeted optimization strategies based on the identified weakness patterns. The iteration control module, connected to the interpretable analysis engine and the optimization strategy generation module, is used to control the iterative process of weakness discovery and optimization until the weakness is significantly improved.

12. The intelligent algorithm weakness discovery and iterative optimization system based on interpretable analysis according to claim 11, characterized in that, The interpretable analysis engine includes: a feature importance calculation unit for calculating the gradient or contribution of each component to the model output; a multi-level analysis unit for selecting an appropriate interpretability method according to the algorithm type, including at least one of Grad-CAM, attention mechanism, and SHAP value analysis; and a component representation generation unit for formatting component information into a structured representation containing category, region, confidence, and importance.

13. The intelligent algorithm weakness discovery and iterative optimization system based on interpretable analysis according to claim 11, characterized in that, The targeted adjustment system includes: a perturbation space definition unit, used to define a five-dimensional perturbation parameter space including component category, perturbation quantity, perturbation type, perturbation intensity, and target confidence change; a perturbation generation unit, used to generate specific perturbation samples based on the perturbation parameters; a binary search optimization unit, used to efficiently determine the minimum effective perturbation intensity; and a combination testing unit, used to test the combined effect of multiple perturbations and identify composite weaknesses.

14. The intelligent algorithm weakness discovery and iterative optimization system based on interpretable analysis according to claim 11, characterized in that, The pattern comparison analyzer includes: a weakness pattern extraction unit, used to extract feature descriptions of weakness patterns from perturbation test results; a statistical analysis unit, used to calculate the occurrence frequency, average impact degree, and impact variance of weakness patterns; a severity scoring unit, used to comprehensively evaluate the severity of weakness patterns; and a pattern classification unit, used to classify and organize weakness patterns according to component type, perturbation type, and impact range.

15. The intelligent algorithm weakness discovery and iterative optimization system based on interpretable analysis according to claim 11, characterized in that, The optimization strategy generation module includes: a data augmentation strategy generation unit, used to generate targeted adversarial samples based on weakness patterns; a loss function adjustment unit, used to add robust regularization terms for weak components to the original loss function; a model architecture adjustment suggestion unit, used to generate model architecture adjustment suggestions to enhance the processing capabilities of weak components; and an effect evaluation unit, used to evaluate the performance improvement of the optimized algorithm.

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