292 results about "Content security" patented technology

Automatically investigating security incidents and generating security incident reports using a Large Language Model (LLM). A computerized system receives an incoming Security Alert Message pertaining to a possible security-related incident. The system automatically feeds into the LLM at least: the content of the Security Alert Message; the metadata of the Security Alert Message; context information describing a security domain; and organization context information pertaining to users and machines of that organization. The system automatically prompts the LLM to automatically investigate the Security Alert Message and to automatically generate a detailed Incident Report pertaining to the Security Alert Message.

The invention discloses a video stream dynamic fragmentation encryption and block chain evidence storage method, and relates to the technical field of video content security, and the method comprises the steps: calculating a color histogram difference value and an optical flow vector change rate between adjacent frames of an input video, marking the difference value as a scene switching point when the difference value exceeds a preset threshold value, and storing the scene switching point; the method comprises the following steps: preliminarily dividing a video into a plurality of scene segments according to scene switching points, performing content complexity evaluation on the scene segments, calculating gray level co-occurrence matrix characteristics of each frame of image through texture density analysis, calculating edge complexity to extract the number and distribution of Canny edges, and performing motion vector statistics to analyze the size and direction of inter-frame object displacement. The change rate between adjacent pixels in the color space is measured according to the color change gradient; the video stream dynamic fragment encryption and block chain evidence storage method is suitable for video contents of different types and complexities, and has relatively high detection accuracy and robustness.

The invention belongs to the technical field of digital content security, and discloses a multi-mode AI content security risk traceability and identification detection system. Through obtaining multi-source heterogeneous data of content creation, editing, distribution and presentation of a full link, a content full link feature fingerprint is constructed, and cross-modal consistency analysis and link continuity verification are realized. According to the method, traceability map construction is introduced, potential tampering points are used as map nodes, content risks are quantified through tampering probability weights and risk transfer intensity, and tampering sources and propagation paths are accurately identified. The method has comprehensiveness and accuracy, deep analysis and risk assessment can be carried out on the multi-modal content, and the tampered content is effectively recognized and synthesized.

The invention discloses a big language model generation content security test system and method in a black box scene. The system comprises a jailbreak prompt word library module used for storing jailbreak prompt words for performing security test on a big language model; the violation question and answer pair module is used for storing violation question and answer pairs covering different types; the response acquisition module is used for obtaining a data request packet according to query content formed by the jailbreak prompt word and the query request; the security analysis module is used for calculating the similarity between response data corresponding to the query request and an expected violation answer, taking the similarity as a security score, and inputting the security score into the adaptive optimization module; and the adaptive optimization module is used for optimizing the jailbreak prompt words output by the jailbreak prompt word bank module by using a genetic algorithm according to the security score output by the security analysis module. According to the method and the device, the security of the large language model generation content can be effectively tested.

The application discloses a feature editing method for large model content security, which compares and analyzes the sparse coding features of a chat assistant constructed based on a large language model under positive user input and negative user input, extracts the internal response differences of the model to different semantic directions, and the mechanism can automatically and accurately identify the key feature dimensions highly related to the semantic direction of the target attribute. The model activation is mapped to a sparse feature space by using a sparse autoencoder, and each dimension of the feature has independent and interpretable semantic meaning. By injecting a feature guide vector in the space, the interference of the control process on the text grammar, fluency and information density is significantly reduced. The sparse representation mechanism is introduced to structure the intermediate activation features in the reasoning process of the large language model and to intervene in a targeted manner, so that the reply of the chat assistant to the user input conforms to the preset safety specification, and the safety and controllability of the chat assistant in the interaction with the user are improved.

The present invention is directed to enhanced content security mechanisms for containers. Further, the invention is directed to containers comprising these enhanced content security mechanisms. In particular, the containers of the present invention comprising the enhanced content security mechanism of the invention offer advanced anchoring of the contents (e.g., baked goods or sensitive devices such as medical devices) of the container by preventing: shifting, tipping, breaking, crumbling, rolling or other movement of the contents; thus securing the contents and preventing damage to the contents, i.e., preserving the appearance and integrity of contents contained within.

The invention relates to the technical field of computers, and discloses a model content security control method and system, electronic equipment and a storage medium, and the method comprises the steps: combining a received user input with a historical dialogue record of a current session to form a dialogue context; performing risk identification based on the dialogue context, generating at least one risk category and a corresponding initial risk value, and generating a risk integral corresponding to the current input according to a preset integral strategy; accumulating the risk points to accumulated risk points of the current session; comparing the accumulated risk integral with a dynamic risk threshold value obtained by calculation according to a reference threshold value, a logarithmic function attenuation item of the dialogue round and a user historical behavior adjustment item; and when the accumulated risk integral reaches or exceeds a dynamic risk threshold value, triggering a preset risk management and control measure. According to the method, the risk content can be effectively identified and controlled, attacks bypassing a security mechanism through slow induction can be defended, and the security boundary is not easy to detect.

The invention provides a large model dynamic protection method and system based on a zero-trust architecture. The method comprises the steps that a security proxy gateway receives an access request; authenticating an initiating main body of the access request, collecting context information and transmitting the context information to a strategy decision point; the strategy decision point calculates a trust score in real time based on a dynamic trust evaluation model and performs real-time evaluation in combination with an access control strategy to generate a dynamic authorization judgment result; if the access is allowed, forwarding the access request to a large language model server, and performing input security filtering; the large language model server generates response content and performs output security filtering; and returning the final response subjected to the output security filtering to the initiating main body through the security proxy gateway. According to the method, a multi-layer protection framework is constructed, a dynamic trust evaluation model is introduced, and a content filtering layer is deployed, so that continuous permission verification, risk adaptive control and full-link content security protection are realized, and the service security of a large model is effectively guaranteed.

The present application discloses a content security recognition method based on the integration of multiple large language models, belonging to the technical field of data processing. The method includes: receiving a query text, performing word segmentation processing on the query text to generate query text word segments, obtaining sensitive data texts matching the query text word segments in a sensitive database, splicing the query text and the sensitive data texts based on a preset prompt word template to generate a security recognition prompt word for the query text, inputting the security recognition prompt word into at least two large language models, obtaining the output results of the large language models, integrating the output results, and determining the security recognition result of the query text. The present application recalls sensitive data texts through a sensitive database, enhances the security recognition prompt words of the large language models, and improves the accuracy of sensitive word recognition through the integrated processing of multiple large language models.

The invention relates to the technical field of virtual reality interaction, and discloses a virtual reality multi-person interaction method, which comprises the following steps: realizing environment synchronous initialization based on space anchor point calibration and dynamic object loading, and configuring value physical rules and interaction logic; executing a data synchronization mechanism by establishing connection and session management; performing real-time action synchronization and interaction logic processing according to user input processing and action capture; user data privacy and security are protected, and content security and user behaviors are supervised. According to the virtual reality multi-user interaction method and system, by adopting a distributed server architecture and dynamically distributing the nearest edge node according to the geographic position of the user, dynamic load balancing can be realized, QoS priority division is started, low-delay transmission is realized by adopting a UDP + RUDP protocol, communication data is encrypted by adopting an AES-256-GCM, and a key is dynamically updated by adopting a dual-ratchet protocol; and the biological characteristics are converted into irreversible hash values, so that the security of privacy data of the user can be effectively improved.

The invention relates to the technical field of multi-modal content security detection, in particular to a video generation content credibility detection method and system. The method comprises the following steps: dividing an input video into non-overlapping segments, independently modeling through three paths of spatial appearance, motion and semantics, and constructing a unified dynamic representation tensor after alignment and fusion; a cross-fragment bidirectional residual evolution mechanism is introduced to analyze time sequence splitting abnormity, Gaussian perturbation is injected to quantify disturbance response, and fragment vulnerability is evaluated in combination with a residual stability index; quantizing a fragile region by combining confrontation disturbance and designing a multi-scale consistency kernel mechanism to generate a credible score, and judging the authenticity of the video by combining an adaptive threshold value; secondary disturbance is adopted to enhance confidence for fuzzy samples, continuous self-learning optimization is realized through pseudo tag guidance, counterfeit style clustering and model fine tuning, and generalization ability for new counterfeit samples is improved. According to the invention, through multi-dimensional dynamic modeling and a self-learning mechanism, the accuracy and robustness of video credible detection are significantly improved.

The invention discloses a multi-modal video content security examination method, which comprises the following steps of: performing multi-modal data extraction on a to-be-detected video, and performing multi-modal feature alignment; inputting each mode into a pre-trained cross attention and MoE mechanism-based fusion model, wherein the fusion model is used for linking the current single mode with information of other modes through a bidirectional cross attention mechanism and obtaining fusion features of the current mode; and carrying out pooling or clustering operation on the fused features of each mode, then carrying out feature splicing, inputting the spliced features into a classification network model for classification to obtain a classification result, and judging whether the content is illegal or not according to the classification result. According to the method, bad information of visual, text, sound and other modes contained in the video is detected through the multi-mode fusion large model, and feedback is given, so that propagation of bad content is prevented.

The invention discloses an AI-driven convergence media digital audio and video content security auditing method, which comprises the following steps: acquiring audio and video contents published by a platform and user information of a publisher in real time, the audio and video contents comprising live streaming, video files, audio files and image-text contents; feature extraction is carried out on the video file, the text data, the video data and the audio data, and then fusion and vectorization processing are carried out to obtain a comprehensive feature vector; and inputting the comprehensive feature vector into an AI model for detection, determining whether illegal content exists or not, and processing according to a detection result. The AI technology is introduced, intelligent auditing of various modal contents such as videos, audios and images and texts is achieved, the auditing efficiency and accuracy are improved, meanwhile, the auditing strategy is dynamically adjusted according to different scenes and user behaviors, the diversified content auditing requirements of the convergence media platform are met, and the safety and compliance of digital audio and video contents are ensured.

The invention relates to the technical field of natural language processing, in particular to an AIGC content safety monitoring system and method based on dynamic reasoning and context awareness, and the system comprises a semantic graph construction module which is used for extracting entity nouns and predicate verbs in a text according to a received AIGC interaction text flow, generating a semantic concept node set, and sending the semantic concept node set to a database; and performing directed connection and hierarchical nesting on the concepts in the semantic concept node set according to a logic direction according to a subject-predicate-object dependency relationship rule. According to the method, the curvature value of the semantic track is calculated, and the similarity between the direction vector and the center of the sensitive semantic cluster is combined for double verification, so that sudden turning of an intention in a dialogue process or progressive induction to a sensitive field can be perceived, and abnormal mutation can be recognized through a curvature pulse form; therefore, hostile attack behaviors are accurately captured in real time in dynamic interaction, and the defense capability for context dependent attacks and implicit induction behaviors is improved.

The invention relates to the field of large model security testing, and particularly provides a large model output content security testing method and device, and the method comprises the following steps: S1, preparing and managing a test set, a sensitive word library and a regular expression which are required by testing; s2, reading a test set, and obtaining a large model output result according to the test set and the large model interface information; s3, judging whether the output content of the large model is safe or not according to the sensitive lexicon and the regular expression; s4, extracting semantic risk features according to the output content of the large model by using the large model and the oriented Prompt, and automatically storing the semantic risk features after confidence verification; and S5, storing the information result of each request in a file. Compared with the prior art, the test time can be shortened, and the evaluation efficiency can be improved; and the security of the output content of the large model can be effectively evaluated by using a method for dynamically constructing the sensitive word bank by using the output result of the large model.

The invention discloses a large model content security-oriented feature editing method, which comprises the following steps of: comparing and analyzing sparse coding features of a chat assistant constructed on the basis of a large language model under positive user input and negative user input, and extracting internal response differences of the model in different semantic directions; the mechanism can automatically and accurately identify key feature dimensions highly related to the semantic direction of the target attribute. A sparse auto-encoder is utilized to activate and map the model to a sparse feature space, and each dimension of feature has an independent and interpretable semantic meaning. By injecting a feature guide vector into the space, the interference of a control process on text grammar, fluency and information density is remarkably reduced. A sparse representation mechanism is introduced, structural modeling and targeted intervention are carried out on intermediate activation features in a big language model reasoning process, replies input by a chat assistant to a user are guided to conform to a preset safety specification, and the safety and controllability of the chat assistant in interaction with the user are improved.

The invention provides a large-model content security multi-level defense method. The method comprises the steps that an input-processing-output full-link multi-stage cooperative defense framework is constructed, and the full-link multi-stage cooperative defense framework receives to-be-detected user input data and transmits sensitive word detection passing content to an intention recognition module; the intention identification module judges the risk level of the user input data, and routes the user input data identified as medium and high risks to the risk label classification module; the risk label classification module performs risk category classification on the user input data, and transmits the user input data subjected to risk category classification to the security enhancement module; and the security enhancement module generates compliant system response content through a domain fine tuning and reinforcement learning strategy, and returns the system response content to the user after performing security interception processing on the system response content. Through hierarchical defense, dynamic routing and security enhancement module training, a large model security enhancement scheme covering the whole process of input, reasoning and output is constructed.

The invention provides a text classification method and a deep learning model training method and device, and relates to the technical field of artificial intelligence, in particular to the technical field of deep learning, natural language processing, content security and intelligent search. According to the specific implementation scheme, the method comprises the steps of generating semantic features of an input text by using a shared base network; according to the semantic features and a splicing weight matrix of the plurality of tower networks, determining respective classification results of a plurality of text classification tasks respectively corresponding to the plurality of tower networks, the splicing weight matrix being obtained by splicing weight matrixes of the same processing layer of the plurality of tower networks; and according to respective classification results of the plurality of text classification tasks, determining a target classification result of the input text.

The invention relates to the technical field of video analysis, in particular to a video stream multi-mode content security dynamic auditing system and method. According to the invention, through the conjoint analysis of the hue ratio change of the continuous image frames and the focus area heat map, the hue sudden change and concentrated concern area in the video image can be accurately identified, the potential violation fragment can be effectively positioned, and on this basis, the interaction behavior analysis mechanism of the image and audio bimodal data is introduced, so that the accuracy of the image and audio bimodal data interaction behavior analysis is improved. Therefore, the recognition of the sudden behavior is not limited to a single visual or auditory clue any more, the modal jump behavior segment is constructed when the image action and the voice change are synchronously highlighted, and then the time sequence overlapping point location in the modal sudden change process is further captured, so that the positioning precision of the abnormal interruption behavior is enhanced, and the positioning accuracy of the abnormal interruption behavior is improved. And multi-dimensional measurement and scoring of the burst area are realized in combination with trend information of frame-level dominant weight change, so that the recognition coverage rate and the response capability of dynamic content auditing on violation behaviors are improved.

The invention provides an HLS private encryption rapid play starting method and system. The system comprises a server module used for generating a secret key, encrypted video content and an m3u8 file; the client module is used for requesting and decrypting video content and executing security protection, the client module requests an encrypted m3u8 file from the server according to a video URL, and the server returns the encrypted m3u8 file after verifying the legality of the request through legal login; the client side module analyzes the m3u8 file and requests the corresponding encrypted TS slice according to the encrypted information, and the server side provides encrypted video content for the client side through the source station or the CDN; and the storage module is used for storing the encrypted TS slice and the associated m3u8 file. Through a series of innovative designs, on the premise that the video content security is guaranteed, the playing efficiency and the user experience are greatly improved, meanwhile, the complexity and the maintenance cost of the system are reduced, and remarkable technical advantages and practical value are achieved.

The invention discloses a file anti-desensitization self-learning recognition system and method based on information entropy, belongs to the technical field of intersection of natural language processing and content security recognition, and is applied to document screening and risk recognition in a multi-task scene. The implementation method comprises the following steps of: 1, performing character recognition and noise reduction processing on an original file to form a data set; 2, training labeled sample data through small samples, respectively adopting probability distribution of a data sliding window and information entropy to carry out maximum and minimum normalization screening, and further utilizing a fitted linear regression model to form an anti-desensitization word list; 3, screening the anti-desensitization degrees of the sentence segments of the data set by adopting a dictionary tree Trie structure to form an anti-desensitization sentence segment table; 4, marking the chapter-level anti-desensitization degree data set text fragments by using the large model; 5, generating an anti-desensitization report according to the anti-desensitization word and the anti-desensitization degree of the marked anti-desensitization file; compared with the prior art, the anti-desensitization file screening method and device have the advantage that the anti-desensitization file screening accuracy is improved.

The invention discloses a large model generation content risk identification and intervention method based on deep learning, and the method comprises the steps: collecting multi-modal data outputted by a content generation system, carrying out the preprocessing of the multi-modal data, and generating the preprocessed multi-modal input data; encoding and fusing the features of the multi-modal perception model to generate global risk feature representation data; inputting global risk features into a hunting optimization algorithm, and optimizing model structure parameters, risk thresholds and intervention strategy parameters; optimizing parameter-driven risk identification and hierarchical intervention, and outputting risk levels, categories and hierarchical intervention measures; an intervention effect and user feedback are collected, and continuous optimization and self-adaptive evolution of parameters are driven. According to the method, efficient risk identification and intelligent intervention on the large model generation content are realized, and the accuracy and the automation level of content security management are remarkably improved.

The invention discloses a big data-based civil and commercial law learning recommendation method and system, and the method comprises the following steps: obtaining user learning behavior data and civil and commercial law knowledge base data, and constructing a multi-dimensional learning feature data set; performing standardization processing on the multi-dimensional learning feature data set to generate a structured feature matrix; constructing a user knowledge mastery degree portrait based on the structured feature matrix, and generating a user-knowledge point association graph; constructing a mixed recommendation model according to the user-knowledge point association graph, and generating a personalized learning path; and receiving user feedback data in real time, updating recommendation model parameters, and dynamically adjusting a learning path. According to the method, data refined modeling is realized through a multi-dimensional learning feature data set and an improved TF-IDF algorithm; optimizing learning path adaptation based on a dynamic weight fusion recommendation model and a real-time feedback mechanism; high concurrent processing and content security are guaranteed by means of a modular architecture and a compliance verification unit, and finally an efficient, self-adaptive and compliance intelligent recommendation system is constructed.

The invention discloses a model evaluation method and device, equipment, a storage medium and a product, relates to the technical field of artificial intelligence, and discloses a method for determining a to-be-evaluated model, an evaluation data set and a judgment large model corresponding to a model evaluation task in response to a starting instruction of the model evaluation task; generating a reply of each risk problem in the evaluation data set through the to-be-evaluated model, and performing content security evaluation on the reply of each risk problem through the judgment large model; and generating a safety evaluation result of the to-be-evaluated model based on the content safety evaluation result correspondingly replied by each risk problem. The method can effectively improve the model evaluation efficiency.

The invention discloses an airport terminal display screen content auditing method, device and equipment and a medium, and particularly relates to the technical field of content auditing, and the technical key points are as follows: calculating respective fragment hash values of a plurality of processed fragment contents; verifying the consistency of the to-be-displayed content by using a Byzantine fault-tolerant mechanism based on the respective fragment hash values of the plurality of fragment contents; after the consistency verification is passed, using a security probability calculation function to calculate security probabilities of the plurality of fragment contents to obtain a content security probability of the to-be-displayed content, and outputting a broadcast control instruction after the content security probability meets a preset condition; acquiring a hash value of the broadcast control instruction, and judging the integrity of the broadcast control instruction by using the hash value of the homologous instruction in the historical database; after the judgment is passed, performing signature encryption on the broadcast control instruction, and uploading the encrypted instruction to a block chain for evidence storage after encryption; and if the evidence storage is successful, issuing the broadcast control instruction to the advertisement screen execution terminal to play according to the broadcast control instruction.

The invention provides a content security processing method and device, a storage medium and electronic equipment, and is applied to the technical field of artificial intelligence. Then progressive detection and interception are carried out on dominant risks, multi-modal contradictions and compliance risks in a model reasoning stage by utilizing a hierarchical decision hook, feature matching is realized in combination with a dynamic compliance detection engine, and output is allowed only under the condition that the content is compliant, so that real-time monitoring and timely filtering of the generated content are realized, and the generation efficiency is improved. And the security of the generated content is improved.

The invention discloses an industry large model content security capability construction method and device, equipment and a medium, and relates to the technical field of computers. Comprising the following steps: executing an embedded coding operation on knowledge base entries in a safe knowledge base through a bidirectional encoder model to obtain vector data containing original text literal meaning and semantic features of the knowledge base entries, and storing the vector data in a preset vector database; creating a blacklist library and a white list library based on a regular matching technology, and judging a risk level of a user input problem based on data stored in the blacklist library and the white list library; and selecting a corresponding answering mode based on the risk level of the question input by the user. Therefore, on the premise that user interaction experience is not affected, hint word attacks can be accurately recognized and intercepted, and it is ensured that large model generation content is safe and reliable.

The invention introduces an advanced AI-driven anti-piracy system that safeguards audio- video streaming content by generating unique content variations through programmable adjustments to camera cut time codes within a structured list of instructions describing edits of audio-video content. It employs a sophisticated production pipeline to create and distribute these variations ensuring tailored delivery to individual viewer conditions. A recorder meticulously records the distribution of manifest files, enabling the identification of potential sources of piracy. The system includes a detection component that utilizes advanced Artificial Intelligence algorithms to compare the original content with potentially pirated versions, allowing for the effective identification and tracking of unauthorized distributions. This approach represents a significant advancement in combating digital piracy, ensuring content security while maintaining high-quality viewing experiences.

The invention provides a content security auditing method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: if it is determined that a scene category of an input text belongs to an exemption security auditing scene set, sending the input text as a question and answer prompt word to a question and answer large model for answering; otherwise, when it is determined that the initial risk level of the input text is a low risk, inputting the input text into the security auditing model to determine the final risk level of the input text; and if the final risk level is low risk, sending the input text as a question and answer prompt word to the question and answer large model for answering. According to the method, the differentiated auditing processing capability of strict interception of high-risk content, intelligent rewriting of medium-risk content and rapid release of low-risk content based on the scene and the user portrait is realized in a high-concurrency scene, the balance between auditing efficiency and safety is effectively balanced, and the safety of the user is improved through double-layer auditing of a safety word library rule and a safety auditing model. And the auditing accuracy and flexibility are improved.

The invention belongs to the technical field of content auditing, and provides a content security auditing method and system based on big and small model mixing, and the method comprises the steps: carrying out the content security auditing of input content through a teacher big model, and generating an initial auditing result; auditing the initial auditing result and correcting error data to form a training data set; training the student small model based on the training data set, fitting the detection capability of the teacher large model, deploying as an actual content security auditing model, and outputting an auditing result after training; and performing auditing monitoring on an auditing result output by the student small model, and if the auditing is not passed, finely adjusting the teacher large model by using data which is not passed, and updating the student small model. A small student model is selected to fit a teacher model, the calculation amount is greatly reduced while the auditing capability is reserved, and the auditing efficiency is improved; meanwhile, manual auditing and feedback adjusting mechanisms are reserved, the model is ensured to adapt to the current auditing standard, and the final auditing result meets the requirements of auditing personnel.