Method for detecting a violation condition, controller, medium and product

Abstract

The application provides a method for detecting a violation condition, a controller, a medium and a product, and is applied to the tobacco technical field. The method comprises the following steps: acquiring an image; performing preprocessing on the image to obtain a preprocessed image; performing feature extraction on the image to obtain color features, text features and scene features; performing feature fusion on the color features, the text features and the scene features to obtain fused features; performing identification and analysis according to the fused features to obtain identification and analysis results; and determining a violation condition of the image according to the identification and analysis results. Since the application can perform feature extraction, feature fusion, identification and analysis on the image to obtain identification and analysis results, and then determine the violation condition of the image according to the identification and analysis results, the application can detect network violation of tobacco sales.

Description

Technical Field

[0001] This application relates to the field of tobacco technology, and in particular to a method, controller, medium, and product for detecting violations. Background Technology

[0002] In related technologies, the current search and governance of illegal online cigarette sales is an important task of tobacco monopoly management. However, traditional search methods mainly rely on manual searches, which are extremely inefficient, like finding a needle in a haystack when faced with massive amounts of content. Furthermore, they are highly subjective and difficult to scale. A common manual search method involves keyword searches, such as matching simple text keyword databases (e.g., "cigarettes," "food," "duty-free cigarettes") on the platform. This method is easily circumvented. Violators use homophones, slang, special symbols, and image-based text to bypass detection. Moreover, cigarette images are often carefully disguised, blending well with the background or showing only a portion of the image, making it impossible to distinguish between "display" and "sales intent," and failing to associate the product with the sales context (e.g., price, contact information, "shipping available"), resulting in low accuracy of traditional methods. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method, controller, medium, and product for detecting violations, aiming to detect illegal online cigarette sales.

[0004] Firstly, embodiments of this application provide a method for detecting violations, the method comprising: Acquire images; The acquired image is preprocessed to obtain the preprocessed acquired image; Feature extraction is performed on the acquired images to obtain color features, text features, and scene features; The color features, text features, and scene features are fused to obtain fused features; Based on the fusion features, identification and analysis are performed to obtain identification and analysis results; The violation status of the acquired image is determined based on the identification and analysis results.

[0005] According to some embodiments of this application, the preprocessing includes normalization, watermark removal, and super-resolution reconstruction. The preprocessing of the acquired image to obtain the preprocessed acquired image includes: The acquired image is normalized, watermarked, and super-resolution reconstructed to obtain the acquired image after normalization, watermarking, and super-resolution reconstruction.

[0006] According to some embodiments of this application, the step of extracting features from the acquired image to obtain color features, text features, and scene features includes: Visual features are extracted from the acquired images to obtain the visual features; Text features are extracted from the acquired image to obtain the text features; Scene features are extracted from the acquired images to obtain the scene features.

[0007] According to some embodiments of this application, the identification and analysis result includes identification results and analysis results. The step of identifying and analyzing based on the fused features to obtain the identification and analysis result includes: The fusion features are identified to obtain the identification result; When the recognition result indicates that the fused feature is related to the target object, the fused feature is analyzed to obtain the analysis result.

[0008] According to some embodiments of this application, determining the violation of the acquired image based on the identification and analysis results includes: If the analysis results indicate that there is an intention to sell, it is determined that the collected images contain illegal information.

[0009] According to some embodiments of this application, the method further includes: When violations are found in the acquired images, an evidence report is generated for the acquired images, and an early warning is issued based on the evidence report.

[0010] According to some embodiments of this application, generating an evidence report from the acquired image includes: Obtain the release information of the acquired images; An evidence report is generated based on the published information, the fusion features, and the identification and analysis results.

[0011] Secondly, embodiments of this application provide a controller, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the violation detection method described in the first aspect when running the computer program.

[0012] Thirdly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions for performing the violation detection method as described in the first aspect above.

[0013] Fourthly, embodiments of this application provide a computer program product, including a computer program or computer instructions, wherein the computer program or computer instructions are stored in a computer-readable storage medium, a processor of a computer device reads the computer program or computer instructions from the computer-readable storage medium, and the processor executes the computer program or computer instructions, causing the computer device to perform the violation detection method as described in the first aspect above.

[0014] According to the technical solution of the embodiments of this application, at least the following beneficial effects are achieved: This application proposes a method, controller, medium, and product for detecting violations, applied in the field of tobacco technology. The method includes: acquiring a captured image; preprocessing the captured image to obtain a preprocessed captured image; extracting features from the captured image to obtain color features, text features, and scene features; fusing the color features, text features, and scene features to obtain fused features; identifying and analyzing the fused features to obtain identification and analysis results; and determining the violation status of the captured image based on the identification and analysis results. Since this application can obtain identification and analysis results by performing feature extraction, feature fusion, identification, and analysis on the captured image, and thereby determine the violation status of the captured image based on the identification and analysis results, it can achieve the detection of illegal online tobacco sales and improve the accuracy of detection.

[0015] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0016] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0017] Figure 1 This is a flowchart of a method for detecting violations provided in one embodiment of this application; Figure 2 This is a flowchart of a method for detecting violations provided in another embodiment of this application; Figure 3 This is a flowchart of a method for detecting violations provided in another embodiment of this application; Figure 4 This is a flowchart of a method for detecting violations provided in another embodiment of this application; Figure 5 This is a flowchart of a violation detection method provided in an overall embodiment of this application; Figure 6This is a schematic diagram of a controller for performing a violation detection method according to an embodiment of this application. Detailed Implementation

[0018] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0019] In the description of this application, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0020] In the description of this application, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0021] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0022] In some cases, detecting and addressing illegal online cigarette sales is a crucial task for tobacco monopoly management. However, traditional search methods primarily rely on manual searches, which are extremely inefficient, like finding a needle in a haystack when faced with massive amounts of content. Furthermore, these methods are highly subjective and difficult to scale. A common manual search method involves keyword searches, such as matching simple text keywords (e.g., "cigarettes," "food," "duty-free cigarettes") on the platform. This method is easily circumvented. Violators use homophones, slang, special symbols, and image-based text to bypass detection. Moreover, cigarette images are often carefully disguised, blending seamlessly with the background or showing only a portion of the product, making it impossible to distinguish between "display" and "sales intent." It also fails to correlate the product with the sales context (e.g., price, contact information, "shipping available"), resulting in low accuracy for traditional methods.

[0023] Based on the above, this application proposes a method, controller, medium, and product for detecting violations, aiming to detect illegal online cigarette sales.

[0024] The various embodiments of the detection method for violations of this application will be further described below with reference to the accompanying drawings.

[0025] like Figure 1 As shown, Figure 1 This is a flowchart of a violation detection method provided in one embodiment of this application; the violation detection method may include, but is not limited to, steps S110, S120, S130, S140, S150 and S160.

[0026] Step S110: Acquire the captured image; Step S120: Preprocess the acquired image to obtain the preprocessed acquired image; Step S130: Extract features from the acquired image to obtain color features, text features, and scene features; Step S140: Perform feature fusion on color features, text features, and scene features to obtain fused features; Step S150: Identify and analyze based on the fusion features to obtain the identification and analysis results; Step S160: Determine the violation status of the acquired image based on the identification and analysis results.

[0027] In one embodiment, an image is acquired; the acquired image is preprocessed to obtain a preprocessed image; features are extracted from the acquired image to obtain color features, text features, and scene features; the color features, text features, and scene features are fused to obtain fused features; recognition and analysis are performed based on the fused features to obtain recognition and analysis results; and the violation status of the acquired image is determined based on the recognition and analysis results. Because this application can obtain recognition and analysis results by performing feature extraction, feature fusion, recognition, and analysis on the acquired image, and thereby determine the violation status of the acquired image based on the recognition and analysis results, it can achieve the detection of illegal online cigarette sales and improve the accuracy of detection.

[0028] It is understood that the images collected mentioned above can be the main image of the product, screenshots of the details page, short video frames, and images accompanying blog posts. Images can also be collected according to actual needs, and this application embodiment does not specifically limit them.

[0029] Understandably, this application acquires images by using keyword databases to search on various platforms and by crawling search results and detail pages.

[0030] It is understandable that preprocessing the acquired images can improve their image quality.

[0031] It is understandable that preprocessing of acquired images, including normalization, watermark removal, and super-resolution reconstruction, is performed to improve the image quality of the acquired images.

[0032] In addition, such as Figure 2 As shown, Figure 2 This is a flowchart of a method for detecting violations provided in another embodiment of this application; regarding the above step S130, it may include, but is not limited to, steps S210, S220 and S230.

[0033] Step S210: Extract visual features from the acquired image to obtain visual features; Step S220: Extract text features from the acquired image to obtain text features; Step S230: Extract scene features from the acquired images to obtain scene features.

[0034] Understandably, visual features, text features, and scene features are extracted from the preprocessed acquired images to obtain color features, text features, and scene features.

[0035] For example, visual feature extraction: Use an object detection model to detect tobacco products (cigarette boxes, cigarette sticks, e-cigarettes, cartridges, etc.) that may exist in the acquired image, and make annotations in the acquired image, such as specific features like cigarette boxes, prices, QR codes, contact information text areas, etc. Text Feature Extraction: Detects product titles, descriptive text (such as "good taste," "authentic"), prices, promotional information, contact information, and lead generation phrases (such as "+V," "private chat") in captured images using a text feature extraction model. Scene feature extraction: Using a scene recognition model, determine whether the overall scene of the image is a scene with sales intent, such as product display, advertising poster, chat screenshot, or warehouse stockpiling.

[0036] It is understood that the target detection model described above can be set according to actual needs, and this application embodiment does not impose specific limitations on it.

[0037] It is understood that the text feature extraction model mentioned above can be the PaddleOCR model, which can be set according to actual needs. This application does not impose any specific limitations on it.

[0038] It is understood that the scene recognition model mentioned above can be the EfficientNet model, which can be set according to actual needs. This application does not impose any specific limitations on it.

[0039] In addition, such as Figure 3 As shown, Figure 3This is a flowchart of a method for detecting violations provided in another embodiment of this application; regarding the above step S150, it may include, but is not limited to, steps S310 and S320.

[0040] Step S310: Identify the fused features to obtain the identification results; Step S320: When the recognition result indicates that the fused features are related to the target object, the fused features are analyzed to obtain the analysis results.

[0041] It is understood that the target object mentioned above can be a tobacco-related object, and can be set according to actual needs. This application embodiment does not specifically limit it.

[0042] For example, if the recognition result indicates that the fused features are related to a tobacco-related object, the fused features are analyzed to obtain the analysis results, and the violation status of the acquired image is determined based on the analysis results.

[0043] Understandably, if the analysis results indicate an intent to sell, it suggests that the images were collected in violation of regulations.

[0044] Understandably, if the fused features include words with clear sales intent such as price, discount, contact information, delivery feedback, "sales," "wholesale," and "personal shopping agent," and / or if price tags, QR codes (especially WeChat / Alipay QR codes), multiple boxes of stockpiled goods, etc. are detected, and / or the scene is classified as "product display" or "advertisement," then it is determined that the collected image has the intent to sell and constitutes a violation.

[0045] It is understandable that when there are violations in the collected images, an evidence report is generated from the collected images, and an early warning is issued based on the evidence report.

[0046] In addition, such as Figure 4 As shown, Figure 4 This is a flowchart of a method for detecting violations provided in another embodiment of this application; the above step "generating an evidence report from the acquired image and obtaining an evidence report" may include, but is not limited to, steps S410 and S420.

[0047] Step S410: Obtain the release information of the acquired images; Step S420: Generate an evidence report based on the published information, fusion features, and identification analysis results.

[0048] Understandably, the published information includes the source URL, the publication time, and the publisher ID.

[0049] For example, for images collected that are determined to have violations, the system automatically extracts image or video clips and saves metadata such as the source URL, publication time, publisher ID, extracted fusion features, and identification analysis results to generate a structured evidence report.

[0050] Based on the violation detection methods of the above embodiments, the following presents an overall embodiment of the violation detection method of this application.

[0051] like Figure 5 As shown, Figure 5 This is a flowchart of a violation detection method provided in an overall embodiment of this application.

[0052] S1. Targeted Acquisition and Preprocessing of Network Data: 1. Target platforms with high rates of illegal tobacco sales and collect publicly available data from these platforms (e.g., e-commerce, Xiaohongshu, Douyin, Weibo) to build a multi-dimensional keyword library. This library includes brand terms ("Zhonghua", "Furongwang"), generic terms ("cigarette", "cigarette", "e-cigarette"), slang and jargon ("fragrant", "food", "yān"), and scenario terms ("daigou", "idle", "excessive gift"). Download images and product details pages that are suspected of containing tobacco products. 2. Targeted data collection: Search the keyword library on each platform, crawl search results and product details pages. Once a violating user is identified, crawl all their historical posts, following lists, and follower lists to uncover related accounts and expand the monitoring scope. 3. Maintain the crawler engine: Build a dynamic IP proxy pool to avoid being blocked due to excessively rapid requests from a single IP. Simulate human mouse movement and page scrolling behavior. For CAPTCHA processing, primarily use OCR to handle simple image CAPTCHAs, supplemented by CAPTCHAs that are extremely difficult to crack manually. 4. Data preprocessing and deduplication: Deduplicate images and text to lay a solid data foundation for subsequent analysis.

[0053] Data collected includes: main product images, screenshots of product details pages, short video frames, and images accompanying blog posts.

[0054] Preprocessing: Normalization, watermark removal, and super-resolution reconstruction (used to process blurry small images) are performed on the acquired images and video frames to improve image quality.

[0055] S2. Multimodal feature extraction: For the preprocessed image, three key features are extracted in parallel using a deep learning model: Visual Feature Extraction: An object detection model is used to detect potential tobacco products (cigarette boxes, cigarette packs, e-cigarettes, cartridges, etc.) in images. The detection capabilities for partial, occluded, deformed, and irregularly shaped packaging have been specifically optimized. The object detection model utilizes web crawlers to collect images from the target platform and annotates each image with specific features such as cigarette boxes, prices, QR codes, and contact information text areas.

[0056] It also detects other related objects, such as price tags, QR codes, barcodes, and contact information (phone numbers, WeChat IDs), etc.

[0057] Text feature extraction (OCR): Product title, descriptive text (such as "good taste", "authentic"), price, promotional information, contact information, lead generation language (such as "+V", "private chat"), etc.

[0058] Scene feature extraction: Using image classification or scene recognition models, determine whether the overall scene of the image is a scene with sales intent, such as product display, advertising poster, chat screenshot, warehouse stockpiling, etc.

[0059] The text feature extraction model uses the PaddleOCR model to accurately extract key textual evidence such as price and contact information from images.

[0060] The scene recognition model uses the EfficientNet model to determine the scene in which the image is located, providing important context for subsequent judgments.

[0061] S3. Fusion Analysis and Violation Detection: The multimodal features extracted from S2 are fused and input into a comprehensive decision model (such as an attention-based neural network or a gradient boosting decision tree model), and inference is performed according to preset multi-level decision rules: Rule 1: Target object recognition. Is the target in the image related to tobacco? (If yes, proceed to the next step; otherwise, skip). Rule 2: Sales Intent Analysis. This involves comprehensively analyzing all extracted modal information (such as visual QR codes, textual prices, and in-scene advertising images) to determine "whether there is an intent to sell." Only if both rules are met simultaneously will a violation be ultimately determined.

[0062] Text features: The extracted text contains words with clear sales intent, such as price, discount, contact information, delivery feedback, "sales", "wholesale", and "personal shopper".

[0063] Visual features: Price tags, QR codes (especially WeChat / Alipay QR codes), and multiple boxes of stockpiled goods were detected simultaneously.

[0064] Scene characteristics: The scene is categorized as "product display" or "advertisement".

[0065] Violation determination: If both Rule 1 and Rule 2 are met, the image / video is determined to be an "illegal online cigarette sales advertisement".

[0066] S4. Source tracing and evidence consolidation: Evidence Packaging: For content deemed to be in violation, automatically extract images or video clips and save metadata such as the source URL, publication time, publisher ID, extracted fusion features, and identification analysis results to generate a structured evidence report.

[0067] Source tracing analysis: Conduct correlation analysis on the same publisher ID to uncover all suspicious content; record the extracted contact information to form a key monitoring list.

[0068] S5. Early Warning and Reporting: Evidence reports and violation alerts are pushed to the visual interface of the regulatory platform in real time, prompting regulatory personnel to review and process them.

[0069] It is worth noting that this application has the following advantages: 1. High degree of automation: The entire process from collection, analysis, judgment to evidence fixation is automated, covering a full-chain solution from proactive discovery, intelligent analysis, evidence fixation to source tracing and early warning, realizing pre-regulation and greatly improving efficiency; 2. Strong resistance to evasion: Targeting variant online content, it comprehensively utilizes visual, textual, and scene features. Even if violators use slang, partial shooting, background camouflage, or other means, it can effectively identify them through multimodal information fusion. 3. Intent-level judgment: It can not only identify "what it is", but also judge "what it does". The two-layer judgment that combines "tobacco product identification" and "sales intent analysis" can accurately distinguish between normal display and illegal sales, and solve the problems of low accuracy and high false alarm of single identification.

[0070] Based on the violation detection methods of the above embodiments, the following presents various embodiments of the controller, computer-readable storage medium, and computer program product of this application.

[0071] like Figure 6 As shown, Figure 6 This is a schematic diagram of a controller for performing a violation detection method according to an embodiment of this application. The controller 700 implemented in this application includes: a processor 710, a memory 720, and a computer program stored in the memory 720 and executable on the processor 710, wherein... Figure 6 The example uses a processor 710 and a memory 720.

[0072] The processor 710 and memory 720 can be connected via a bus or other means. Figure 6Taking the example of a connection between China and Israel via a bus.

[0073] Memory 720, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory 720 may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 720 may optionally include remotely located memories 720 relative to processor 710, which can be connected to controller 700 via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0074] Those skilled in the art will understand that Figure 6 The device structure shown does not constitute a limitation on the controller 700 and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0075] exist Figure 6 In the controller 700 shown, the processor 710 can be used to call the control program stored in the memory 720 to implement the above-described violation detection method. Specifically, the non-transitory software program and instructions required to implement the violation detection method of the above embodiment are stored in the memory 720. When executed by the processor 710, the violation detection method of the above embodiment is executed.

[0076] It is worth noting that since the controller 700 of this application embodiment can execute the violation detection method of any of the above embodiments, the specific implementation and technical effects of the controller 700 of this application embodiment can refer to the specific implementation and technical effects of the violation detection method of any of the above embodiments.

[0077] Furthermore, one embodiment of this application provides a computer-readable storage medium storing computer-executable instructions for performing the aforementioned method for detecting violations. Exemplarily, the above-described method is executed... Figures 1 to 5 The methods and steps in the text.

[0078] It is worth noting that, since the computer-readable storage medium of this application embodiment is capable of executing the violation detection method of any of the above embodiments, the specific implementation and technical effects of the computer-readable storage medium of this application embodiment can be referred to the specific implementation and technical effects of the violation detection method of any of the above embodiments.

[0079] Furthermore, one embodiment of this application also provides a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer program or computer instructions from the computer-readable storage medium and executes the computer program or computer instructions, causing the computer device to perform the aforementioned method for detecting violations. Exemplarily, the above-described method is performed... Figures 1 to 5 The methods and steps in the text.

[0080] It is worth noting that, since the computer program product of this application embodiment can execute the violation detection method of any of the above embodiments, the specific implementation method and technical effect of the computer program product of this application embodiment can refer to the specific implementation method and technical effect of the violation detection method of any of the above embodiments.

[0081] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0082] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0083] In the several embodiments provided in this application, it should be understood that the disclosed systems, instruments, and methods can be implemented in other ways. For example, the instrument embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between instruments or units may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0084] It should also be understood that the various implementation methods provided in this application can be combined arbitrarily to achieve different technical effects.

[0085] The above provides a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for detecting violations, characterized in that, The method includes: Acquire images; The acquired image is preprocessed to obtain the preprocessed acquired image; Feature extraction is performed on the acquired images to obtain color features, text features, and scene features; The color features, text features, and scene features are fused to obtain fused features; Based on the fusion features, identification and analysis are performed to obtain identification and analysis results; The violation status of the acquired image is determined based on the identification and analysis results.

2. The method according to claim 1, characterized in that, The preprocessing includes normalization, watermark removal, and super-resolution reconstruction. The preprocessing of the acquired image to obtain the preprocessed acquired image includes: The acquired image is normalized, watermarked, and super-resolution reconstructed to obtain the acquired image after normalization, watermarking, and super-resolution reconstruction.

3. The method according to claim 1, characterized in that, The step of extracting features from the acquired image to obtain color features, text features, and scene features includes: Visual features are extracted from the acquired images to obtain the visual features; Text features are extracted from the acquired image to obtain the text features; Scene features are extracted from the acquired images to obtain the scene features.

4. The method according to claim 1, characterized in that, The identification and analysis results include identification results and analysis results. The identification and analysis based on the fused features to obtain the identification and analysis results includes: The fusion features are identified to obtain the identification result; When the recognition result indicates that the fused feature is related to the target object, the fused feature is analyzed to obtain the analysis result.

5. The method according to claim 4, characterized in that, The step of determining the violation in the acquired image based on the identification and analysis results includes: If the analysis results indicate that there is an intention to sell, it is determined that the collected images contain illegal information.

6. The method according to claim 1, characterized in that, The method further includes: When violations are found in the acquired images, an evidence report is generated for the acquired images, and an early warning is issued based on the evidence report.

7. The method according to claim 6, characterized in that, The step of generating an evidence report from the acquired images, to obtain the evidence report, includes: Obtain the release information of the acquired images; An evidence report is generated based on the published information, the fusion features, and the identification and analysis results.

8. A controller, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, performs a method for detecting violations as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The device stores computer-executable instructions for performing the method for detecting violations as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program or computer instructions, characterized in that, The computer program or the computer instructions are stored in a computer-readable storage medium, the processor of the computer device reads the computer program or the computer instructions from the computer-readable storage medium, and the processor executes the computer program or the computer instructions, causing the computer device to perform the violation detection method as described in any one of claims 1 to 7.

Images