28 results about "Copyright infringement" patented technology

The invention comprises an infringement commodity detection method and device, equipment and a medium, and the method comprises the steps: responding to a target commodity putaway event triggered by a user, and obtaining a commodity description text and a commodity picture in a commodity detail page of a target commodity; judging whether the commodity description text contains a segmented word of which the similarity with preset trademark text information is greater than a preset similarity threshold value or whether the commodity picture contains a preset trademark brand identifier, and if so, judging that the commodity detail page forms trademark infringement; the relevancy between the commodity description text and the commodity picture and a preset copyright description text set and a copyright picture set is confirmed, and when any relevancy meets a preset condition, it is judged that the commodity detail page forms copyright infringement; and when the commodity detail page is judged to be trademark infringement or copyright infringement, constructing a corresponding infringement detection report and pushing the infringement detection report to the user. According to the invention, comprehensive infringement detection of the commodity detail page is realized, and trademark infringement and copyright infringement can be identified efficiently in real time.

The invention provides an automatic evidence obtaining method and system for software copyright infringement. The method comprises the following steps: firstly, acquiring a multi-language interface calling sequence based on a target software running process to form unified semantic expression data; meanwhile, recording a sound signal of the mainboard of the electronic equipment by using a high-sensitivity sound sensor, and generating an equipment sound feature; performing joint coding on the semantic data and the sound features through a cross-modal alignment mechanism to generate multi-modal associated data; a memory enhancement unit and a machine learning technology are used to automatically learn a corresponding relation between a multi-language interface and sound features, and a cross-modal mapping relation is established; finally, cross-language plagiarism behaviors are matched based on the mapping relation, and software similarity relation data are automatically output. According to the method, code semantic analysis and hardware voiceprint features are combined, so that the cross-language software plagiarism behavior is automatically and accurately detected.

The invention belongs to an infringement recognition method, and provides a three-dimensional model block chain copyright infringement recognition method, system and program product for solving the problems that an existing three-dimensional digital copyright infringement recognition method is low in recognition accuracy, poor in recognition effect in a complex scene and not applicable in a high-precision requirement scene. And calculating the Hamming distance between the copyright fingerprint set of the three-dimensional model to be detected and the copyright fingerprint set of the three-dimensional model registered on the given chain, and judging whether to infringer according to the relationship between the Hamming distance and a preset overall matching rate threshold. Wherein during copyright fingerprint set calculation, key point scales in all vertexes of a three-dimensional model are determined according to the geometric volume and surface complexity of the three-dimensional model, mixed feature descriptors are obtained by combining geometric structures and surface texture changes of local neighborhoods of candidate key points, and corresponding fingerprint abstract values are calculated after Hash processing. And the recognition stability under non-infringement operations such as rotation, zooming, slight editing and the like is remarkably improved.

One embodiment of the present invention relates to a computer-automated system and method for identifying copyrighted source code embedded within other source code files, utilizing advanced semantic analysis techniques. This embodiment of the invention addresses the challenge of detecting both literal and non-literal copies of copyrighted code, including instances where the code has been modified in non-semantic ways, such as through renaming variables, changing formatting, or rearranging code blocks. This embodiment creates semantic embeddings of source code using a large language model (LLM). Each segment of source code is transformed into a high-dimensional vector that captures its semantic essence, rather than its literal text. These vectors are then compared using sophisticated similarity metrics, such as cosine similarity or L2 distance, to determine the likelihood of copyright infringement. This embodiment can operate without direct access to the full source code, thereby enhancing privacy and security. Instead, the system works with embeddings that represent the semantic information of the code, significantly reducing the risk of data exposure. Additionally, this embodiment of the invention includes an optional compression module that further minimizes the data footprint by compressing the semantic vectors, enhancing the system's efficiency and scalability. This embodiment of the invention is particularly suited for use in environments where large volumes of code need to be analyzed quickly and accurately, such as in continuous integration / continuous deployment (CI / CD) pipelines. It provides a robust, scalable, and secure solution for managing copyright compliance in software development, offering significant improvements over traditional text-based or hash-based comparison methods.

The present disclosure describes a system and method for providing automated policy enforcement. The system and method may be implemented by a service provider to enforce a policy related to copyright infringement activities. According to an example, the policy may define a system of penalty (strike) levels for violations of the policy up to a maximum number of strikes. When a notification of a policy violation is received, the system may operate to determine whether to issue a strike in association with the notification. When a determination is made to issue a strike in association with a received notification a set of enforcement actions to perform in association with the issued strike may be selected and executed. The set of enforcement actions may terminate detected copyright infringement activities and reduce or otherwise limit the service provider's liabilities when such copyright infringement activities may occur.

This invention belongs to the field of artificial intelligence technology and discloses a method and device for constructing an artificial intelligence training dataset and detecting copyright infringement. First, an original image dataset is acquired, and a portion of the original images are selected as images to be processed according to a preset watermark embedding rate. Then, motion parallax watermark embedding processing is performed on each image to be processed. First, near and far masks are generated based on the distance from the pixel position to the image center. Then, near and far pixel offsets are applied respectively to obtain near and far offset images, which are fused to obtain a disguised image. This disguised image is then weighted and fused with the image to be processed to obtain a motion parallax watermark image, and its classification label is modified to a preset target label. Finally, it is combined with the unprocessed original images to form a training dataset. The purpose of this invention is to solve the problems in existing data watermarking methods, such as the ease with which training data preprocessing can destroy the watermark, inconsistencies between traditional watermark generation and the characteristics of the original image, leading to watermark failure, inaccurate infringement detection, and easy removal of the watermark, thereby achieving more effective copyright protection and infringement detection.

The invention discloses an advertisement material infringement detection method and device, equipment and a storage medium. The method comprises the following steps: obtaining advertisement putting data and advertisement copyright materials, wherein the advertisement putting data comprises a plurality of advertisement candidate materials; determining a candidate material visual feature corresponding to each advertisement candidate material and a copyright material visual feature corresponding to the advertisement copyright material, and determining a suspected infringement material based on each candidate material visual feature and the copyright material visual feature; candidate material semantic features corresponding to each suspected infringement material and copyright material semantic features of each advertisement copyright material are extracted, infringement confidence is determined based on each candidate material semantic feature and the copyright material semantic features, and the suspected infringement material with the infringement confidence greater than a confidence threshold is determined as an infringement confirmation material. According to the scheme, through multi-dimensional feature extraction and comparison, in combination with deep fusion analysis of visual features and semantic features, the accuracy and robustness of advertisement material infringement recognition are effectively improved.

The application discloses a role copyright infringement detection method and system based on visual and semantic features, the method comprises the following steps: S1, role target separation and feature extraction: input the content to be detected, separate the role target therein, and extract the role features, the role features comprising at least one of visual features, semantic features and time sequence features; S2, role feature correspondence and matching: the extracted role features are matched with the existing role features in the copyright content library; S3, role feature similarity analysis: when the similarity between the extracted role and the matched role exceeds a preset threshold, the role is determined as a potential infringing role. The application takes the visual and semantic features of the role as the core, constructs a whole-process detection framework from target separation to feature matching and then to similarity analysis, can effectively solve the low-efficiency problem of the prior art which depends on the manual comparison method, provides an automatic and intelligent solution for the role copyright infringement identification of large-scale digital content, and significantly improves the detection efficiency and the scientificity of the identification.

The application comprises a method and device for detecting infringement of commodities, equipment and medium, the method comprising: in response to a target commodity listing event triggered by a user, obtaining commodity description text and commodity pictures in a commodity detail page of the target commodity; determining whether the commodity description text contains segmented words with a similarity greater than a preset similarity threshold to preset trademark text information, or whether the commodity pictures contain a preset trademark brand logo; when the commodity detail page contains the segmented words or the commodity pictures contain the trademark brand logo, determining that the commodity detail page constitutes trademark infringement; confirming the relevance between the commodity description text and the commodity pictures and a preset copyright description text set and a copyright picture set; when any relevance meets a preset condition, determining that the commodity detail page constitutes copyright infringement; and when the commodity detail page is determined to constitute trademark infringement or copyright infringement, constructing a corresponding infringement detection report and pushing the report to the user. The application realizes comprehensive infringement detection of the commodity detail page and can identify trademark infringement and copyright infringement in real time and efficiently.

A system comprises one or more processors and one or more memories storing instruction code that, when executed, causes the system to receive audio content from one or more content sources, the audio content including a plurality of audio compositions. For each audio composition, the system identifies one or more stems that represent individual instrumental or vocal components and generates, for both the full composition and the identified stems, a plurality of interpolated stem variants by applying a defined set of pitch adjustments and a defined set of tempo adjustments. The system stores the interpolated variants for the plurality of audio compositions in an interpolation database, receives suspect content to be evaluated for infringement, and compares the suspect content against at least a portion of the interpolated variants stored in the interpolation database to determine whether the suspect content matches any of the interpolated variants. Based on any detected match, the system determines an appropriate action to take, including reporting a match outcome, permitting or blocking distribution of the suspect content, or preparing material for rights registration.

We provide the system. [Solution] An interface means for receiving information data entered by the user, An analytical means for comparing the aforementioned information data with an existing information collection and evaluating the risk of copyright infringement, Based on the results of the analysis means, a generation means generates an alternative when there is a risk of infringement, A display means for presenting the alternative plan and analysis results generated by the generation means to the user, A distribution management means applied to a content distribution service for providing the aforementioned information data, and which has the function of processing the information data in real time, A system that includes this.

The application relates to a model watermark embedding and verification method against model extraction, which comprises the following steps: obtaining original training data set of a target model, superimposing an adversarial disturbance on the training data set to construct a trigger sample, inputting the trigger sample into a clean model without watermark to optimize the adversarial disturbance; selecting a sample subset from the original training data set, superimposing the optimized adversarial disturbance on the sample subset to generate a trigger data set; taking the remaining samples in the training data set as a clean data set, combining the trigger data set and the clean data set to construct a watermark training data set, training a protected model embedded with a watermark through the watermark training data set; verifying a suspected model through the trigger data set, taking the proportion of outputs of the suspected model that do not belong to a target category as a watermark success rate, and determining whether there is a watermark correlation relationship between the suspected model and the protected model. The application can reduce the model copyright infringement risk caused by model extraction attacks.

The present technology pertains to an information processing device, an information processing method, and a program that make it possible to reduce the risk of copyright infringement. In the present invention, generated text composed of tokens generated by sampling is corrected by replacement or resampling on the basis of similarity in terms of copyright infringement on the existing literary work. The present technology can be applied, for example, to the case of generating lyrics or the like.

The application discloses an AI-based training data copyright detection and sensitive information desensitization method and system. In view of the copyright infringement risk and data privacy leakage problem existing in the AIGC model training data set, the application constructs a dynamically updated legal regulations and platform rules knowledge base; the ownership certificate and authorized range of the data are automatically checked by using multi-modal feature extraction and blockchain traceability technology; the infringement risk is quantitatively evaluated by a risk rating model, and compliant replacement materials are automatically generated by using generative AI technology; at the same time, a double-layer adversarial network is used to accurately desensitize sensitive information in the training data.

Systems, methods, and computer-readable storage media for detecting copyrighted material online, and more specifically to search for media content, comparing found content to known, sending notices regarding copyright infringement, and monitoring removal of the infringing material. Doing so involves the use of web crawlers to detect media content, generating fingerprints for that content, and comparing the fingerprints against known proprietary fingerprints. Once proprietary content is identified, the system can send notices out to the owner of the web site where the content was found and can continue monitoring the site until the content is removed.

A system to detect an infringing audio composition is disclosed. The system is configured to receive an audio file from a user; prepare track samples based on the audio file to compare with audio files stored with a digital rights repository; determine an original audio recording from the digital rights repository; prepare an acapella version of the original audio recording and / or an instrumental version based on the original audio recording; process, by iteratively varying a pitch, a tempo and / or a key of, the versions to create a processed iteration of each version; automatically compare the processed iterations of the audio files stored with a digital rights repository; determine a match between the processed iterations and the audio files stored with the digital rights repository; and determine a digital rights action to take based on the match between the processed iteration of each of the versions.

The present application provides an automated evidence collection method and system for software copyright infringement. Among them, the method first collects the calling sequence of multi-language interfaces based on the target software running process to form unified semantic expression data; at the same time, a high-sensitivity sound sensor is used to record the sound signal of the electronic device motherboard to generate the device sound characteristics; then the semantic data and the sound characteristics are jointly encoded through a cross-modal alignment mechanism to generate multi-modal related data; then the memory enhancement unit and machine learning technology are used to automatically learn the correspondence between the multi-language interface and the sound characteristics, and establish a cross-modal mapping relationship; finally, based on the mapping relationship, the cross-language plagiarism behavior is matched and the software similarity relationship data is automatically output. This application realizes the automated and accurate detection of cross-language software plagiarism behavior by combining code semantic analysis with hardware voiceprint features.

Embodiments of the present invention disclose a method and system for detecting music copyright infringement. The method includes identifying music information of a target piece of music to form a feature set of the target piece of music; wherein the music information of the target piece of music includes one or more of the following: song title, audio fingerprint, and lyrics; comparing the identified feature set of the target piece of music with feature sets of music in a copyright database; and determining whether the target piece of music is infringing based on the comparison results. The method and system for detecting music copyright infringement in embodiments of the present invention have wide coverage, low cost, and can promptly detect copyright infringement.

The invention relates to a generative artificial intelligence copyright infringement evaluation method and system. The method comprises the following steps: establishing a to-be-tested model group comprising a control group and an experimental group; performing a question test on the to-be-tested model group to obtain a test answer; and based on the similarity between the test answer and the original work, performing infringement scoring on the to-be-tested model group to obtain an evaluation result. According to the method, the accuracy and comprehensiveness of evaluation are remarkably improved by quantitatively analyzing the overlap ratio of the model output content and the original text.

This invention discloses a method and device for constructing an artificial intelligence training dataset and detecting copyright infringement based on radio frequency (RF) shadows. The method involves selecting a portion of the original image dataset as images to be processed according to a preset watermark injection rate; performing RF shadow watermark embedding processing on each image to obtain an RF shadow watermark image; and modifying the classification label of the RF shadow watermark image to a preset target label. The RF shadow watermark embedding processing includes: generating horizontal, vertical, and radial waveforms based on the pixel positions of the images to be processed; fusing the horizontal, vertical, and radial waveforms according to preset weights to obtain a base waveform; generating a center attenuation envelope to control the waveform intensity distribution; multiplying the base waveform with the center attenuation envelope to obtain a base mask; weighted fusing the base mask with the images to be processed to obtain the RF shadow watermark image; and combining the RF shadow watermark image with the unprocessed original images to constitute an artificial intelligence training dataset. The purpose of this invention is to solve the problems of easily detectable watermarks, poor stability, and limited applicability in existing technologies.

The system according to the embodiment aims to check whether text created by a user infringes the copyright of a third party. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a reporting unit. The reception unit inputs text created by a user. The analysis unit analyzes the text input by the reception unit and checks for similarity with existing copyrighted works. The identification unit identifies parts that may be copyright infringements based on the results of the analysis by the analysis unit. The reporting unit reports the parts that may be copyright infringements identified by the identification unit to the user.

The present application provides a software copyright protection detection method and system based on semantic analysis. The method includes: in the interaction scenario between the user terminal and the software copyright protection detection system, collecting physical behavior signals and semantic feedback behavior data generated by the user operation trajectory; combining with a pre-trained semantic analysis model, performing coupled analysis on the physical behavior signals and semantic feedback behavior data to generate a user intention representation sequence; through a pre-trained reinforcement learning model, mapping the semantic feedback behavior and physical behavior in the user intention representation sequence into an association strength parameter; according to the association strength parameter, combining with a preset dynamic risk determination rule, generating a software copyright protection detection result including a copyright risk index. The present application improves the real-time detection accuracy of software copyright infringement behavior.

Systems, methods, and computer-readable storage media for detecting copyrighted material online, and more specifically to search for media content, comparing found content to known, sending notices regarding copyright infringement, and monitoring removal of the infringing material. Doing so involves the use of web crawlers to detect media content, generating fingerprints for that content, and comparing the fingerprints against known proprietary fingerprints. Once proprietary content is identified, the system can send notices out to the owner of the web site where the content was found and can continue monitoring the site until the content is removed.

A system is provided.SOLUTION: A system comprising: means for automatically filtering generated content; means for assessing a risk of piracy of the generated content; means for blocking the generated content if the generated content contains inappropriate content; and means for notifying a user of the generated content.SELECTED DRAWING: Figure 1

We provide the system. [Solution] A means of evaluating the comprehensiveness of the scope of questions for creating examination questions for educational institutions, Means for analyzing and adjusting the difficulty level of the problem, A means for detecting overlap with past exam questions, Means for verifying plagiarism and copyright infringement, A means of proposing the format of the exam questions, A system that includes means for creating and presenting problems to users to support home learning using educational support robots.

The invention discloses a method for identifying a copyright case object protection range. The method comprises the following steps: 1) acquiring and marking a plurality of copyright infringement cases to obtain a copyright infringement case data set; the annotation information comprises the field range of the works, the object form of the works and the originality of the works; 2) establishing a copyright object protection range judgment model for predicting the field range of the works, the object form and the work originality; the copyright object protection range judgment model comprises an encoder module, a joint attention calculation module, a splicer module, a multi-layer perceptron module and a classifier module; 3) using the copyright infringement case data set to train a copyright object protection range determination model; and 4) inputting a to-be-identified work into the trained copyright object protection range judgment model to obtain a field range, an object form and originality of the work. According to the method, intelligent judgment on the protected range of the copyright object can be realized through the case description information.

A system is provided.SOLUTION: A system for assessing the risk of copyright infringement of an image for commercial use, comprising: means for a user to upload an image to be assessed; means for receiving and initially processing the uploaded image; means for feature extraction using an artificial intelligence model to extract visual features from the image; means for matching the extracted features with an existing database and assessing the match or similarity; means for determining the copyright risk of the image based on the assessment; and means for notifying the user of the determination.SELECTED DRAWING: Figure 1

To provide a piracy check system using a AI.SOLUTION: Means for uploading a work created by a user, means for storing metadata of the work received by a server in a database, means for extracting features from the work by a AI, means for storing the features in the database by the server, means for uploading new content and sending a request for a piracy check by the user, means for receiving the new content and adding the request to a processing queue by the server, means for passing the new content to a AI engine and comparing the new content with an existing work in the database by the AI to analyze a match or a similarity, and means for processing an analysis result from the AI engine by the server; A system comprising: means for generating a potential piracy report; means for sending the report generated by a server to a user's terminal; and means for the user to review the result report and determine the next action.SELECTED DRAWING: Figure 1