230 results about "Content security" patented technology

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 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 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 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 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 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.

The invention discloses a security risk dynamic filtering and feedback control method for large model output content. The method comprises the step of executing a multi-dimensional risk feature library construction operation. According to the method, semantic features are extracted by adopting a BERT-BiLSTM hybrid model, risk modes are automatically found in combination with an improved DBSCAN algorithm, the updating efficiency of a feature library is improved by 40%, feature complementation is achieved through a text / image / voice three-mode heterogeneous network design and a cross-mode attention mechanism, the composite violation content detection accuracy rate reaches 92.3%, and the method has the advantages that the method is simple and convenient to operate and high in practicability. A static channel is adopted to ensure basic rule adherence, a dynamic channel is adopted to capture context risks, a three-level filtering strategy is adopted, illegal content is clear to be completely shielded within 0.3 second, a visual warning label is added to suspected content, edge content is manually rechecked, and the time is shorter than or equal to 5 minutes, so that when the model is output, content safety is ensured, and the probability of illegal content detection is lowered. And the application value of the large model is furthest maintained.

The invention provides a video evidence obtaining method based on Transform and quantum features, and belongs to the technical field of crossing of multimedia content security and computer vision, and the method comprises the steps: S1, collecting multi-modal original data; s2, preprocessing the multi-modal data; s3, single-mode authenticity preliminary detection is carried out; s4, multi-modal feature fusion of quantum optimization is carried out; and S5, multi-modal authenticity comprehensive judgment is carried out. Through multi-mode cooperation and quantum technology innovation, the problems of insufficient robustness, inaccurate positioning and the like of a traditional evidence obtaining technology are effectively solved, and an efficient, accurate and feasible technical scheme is provided for authenticity verification of video content.

The invention discloses a content security reasoning auditing method based on knowledge enhancement. The method comprises the following steps: establishing an attribute system based on illegal content in a pre-established standardized knowledge rule base, and structuring the attribute system into an attribute set; a prompt text is generated based on the attribute set and used for calling a teacher model to generate candidate answers through reasoning, and a synthetic data set is established through the candidate answers and the standardized knowledge rule base; using the synthetic data set to guide the student model to supervise and fine-tune to obtain a content category judgment and explanation text, and then optimizing the student model through reinforcement learning training; reasoning explanations are generated through the student model after training optimization and used for manual rechecking and responsibility tracing, and finally violation category labels are output. On the premise of ensuring detection precision and robustness, judgment reasons and bases are explicitly output, transparency and consistency of auditing results are enhanced, reasoning and deployment cost is remarkably reduced, and comprehensive requirements of an internet platform in the aspects of real-time performance, compliance and interpretability are met.

The invention provides a large model content generation auditing method and system based on man-machine collaboration, and relates to the technical field of large models, and the method comprises the steps: deconstructing the generation process data of to-be-audited content, constructing a content risk propagation map, mapping a multi-level auditing strategy library, and recognizing an uncertainty region needing manual intervention. And obtaining manual auditing judgment and correction operation, forming auditing knowledge precipitation and perfecting an auditing strategy library. According to the method, the content risk source can be accurately positioned, the auditing efficiency is improved, a closed-loop optimized man-machine collaborative auditing mechanism is formed, the misjudgment rate is effectively reduced, and the large model content security is improved.

The invention belongs to the technical field of computer information security, particularly discloses a content security protection method and device based on semantic consistency, and aims to solve the problem that high-level cue word injection attacks are difficult to effectively recognize in the prior art. Carrying out real-time interception on dominant illegal texts through a content filtering module; quantizing generation rationality differences of prompt words between attack and normal language models by using a word vector confusion degree calculation module; evaluating the logic coherence of the sentence structure of the prompt word through a statement semantic consistency judgment module; integrating the multi-dimensional feature data and carrying out risk classification by adopting a machine learning algorithm; and routing the suspected attack request to a value fine tuning model for processing according to a judgment result. According to the technical scheme, high-precision recognition and response to complex cue word injection attacks are achieved, and the content safety protection capacity and compliance guarantee level of a large model in an interaction scene are remarkably improved.

The invention relates to the technical field of network content security, in particular to a network media video sensitive information intelligent identification and early warning system, which comprises a data acquisition module for acquiring network media video data; the video information extraction module performs multi-dimensional analysis on the acquired data to obtain depth features; the AIGC video detection module identifies whether the content is the content generated by the AIGC technology or not according to the depth feature, and if yes, an AI generation mark is marked; the causal inference and evaluation module is used for carrying out causal correlation diagnosis on intention and context, carrying out deep analysis through an anti-fact inference technology, judging whether the intention is sensitive information or not, evaluating dangerousness and obtaining a dangerousness level; and the early warning module takes the minimum intervention cost and the maximum risk control as targets, matches and dynamically generates an optimal intervention scheme from a strategy library in combination with the risk level, and intervenes the sensitive video. Therefore, the problems of incomplete multi-modal information analysis, lack of an AI generation content identification mechanism and the like are solved.

The invention provides a dialogue large model streaming output content safety correction method and related device.According to the dialogue large model streaming output content safety correction method, asynchronous safety detection is conducted on a detected object when dialogue large model output content triggers asynchronous safety detection conditions, and if unsafe content is detected, the detected object is subjected to asynchronous safety detection; calling a security enhancement model, performing security correction on the unsafe content through the security enhancement model, and outputting a text fragment meeting a security standard; and after the security enhancement model generates the first security text fragment, stopping the reasoning process of the dialogue large model, splicing the first security text fragment with the text fragment which is output by the dialogue large model and is verified to be secure, and replacing the output content of the dialogue large model with the spliced text content. The subsequent output content of the dialogue large model is switched into the real-time streaming generation content of the security enhancement model, so that the exposure time of the unsafe content of the dialogue large model is shortened, and meanwhile, the output continuity is kept.

Described herein is a technique to update an edge device deployed in a secure computing network. A repository connected to a public network stores build contents configured to update software installed on the edge device; the public network is inaccessible to devices within the secure computing environment. A second device connected to the public network acquires the build contents in a signed lockbox file. An edge device management service generates a lockbox file containing the build contents and a trusted signer outside the secure computing network signs the lockbox file. The second device connects to secure computing network and establishes communications with the edge device. The edge device verifies the signed lockbox file provided by the second device. Upon verification, the edge device extracts the contents of the signed lockbox file and updates the software installed on the edge device. Both offline and online updating approaches are described.

The invention discloses a multi-modal content real-time detection and dynamic replacement method and system. The method comprises the following steps: buffering an input audio / video stream for a fixed time length and synchronously decomposing the input audio / video stream into a video image, an audio text and a picture text; performing sensitive content detection on the three types of data in parallel, and judging overall violation according to at least one violation; and according to the violation type, selecting a global replacement mode or a local mask mode to carry out real-time replacement processing. The system comprises corresponding function modules. According to the invention, multi-modal decomposition and parallel detection are combined with cross-modal cooperative determination, so that the problem of high missed determination rate of single detection is solved; the detection precision and the real-time performance are balanced through fixed buffering and dynamic replacement strategies; particularly, a text detection and recognition fusion model is adopted to improve the dynamic subtitle recognition rate; wide-temperature hardware and fan-free design are combined, stable operation of the system in severe environments such as outdoors is guaranteed, and accuracy, real-time performance and reliability of content security control are remarkably improved.

The invention discloses a video content protection method and device based on a multi-channel image feature security mark, and relates to the technical field of video content security management, and the method comprises the steps: obtaining an original video, and checking the original video; acquiring a video frame image of the audited video, and performing fusion, dimension reduction, compression and encryption processing on multichannel high-order features of the video frame image through a perceptual hash algorithm, an interpolation hash algorithm, a compression coding principle and a national cipher SM4 algorithm to generate a dynamic security mark; embedding the dynamic security mark into the video frame image to generate an approved video; and extracting the dynamic security mark of the currently played video, and checking the consistency between the feature information extracted from the dynamic security mark and the feature information of the played video to judge whether the video is compliant or not. The video content is protected, the security of the video is improved, the compliance check of the video content is realized, and the accuracy and efficiency of compliance judgment of the video content are improved.

This invention discloses a batch multimodal data alignment method and system based on the CLIP model, belonging to the field of information processing technology. The method includes: S1: receiving batch multimodal data; S2: data preprocessing; S3: batch feature extraction based on the CLIP model to achieve feature alignment; S4: batch classification based on a Prompt template; S5: result generation and output, and visualization processing. This invention is applicable to user behavior analysis, abnormal traffic detection, and network content security governance in a big data communication environment. It fully utilizes CLIP's powerful cross-modal semantic alignment and zero-sample transfer capabilities, combined with batch optimization strategies, dynamic task scheduling, and a batch classification method based on a Prompt template, thereby achieving efficient, flexible, and scalable multimodal data processing, accurately identifying malicious text, malicious images, and cross-modal risk links, and enabling real-time monitoring and intelligent prevention of malicious network behavior.

The invention belongs to the field of computer network content security, particularly relates to a text compliance real-time verification method and system based on a two-dimensional knowledge base, and aims to solve the problems that in the prior art, accuracy and adaptability are difficult to consider at the same time, and scene verification capacity is lacked. The method comprises the following steps: acquiring a to-be-verified text and scene information thereof; generating a scene verification strategy based on the scene information and a preset multi-dimensional feature map; based on a two-dimensional knowledge base, executing two-way parallel verification on the to-be-verified text, the two-dimensional knowledge base comprising an explicit rule knowledge base and a semantic context risk knowledge base, so as to obtain a rule verification result and a semantic verification result; and according to a scenarized verification strategy, performing fusion decision on a rule verification result and a semantic verification result, and outputting a final compliance judgment conclusion. According to the method, the rule model and the semantic model are combined, and the scene dimension is introduced for dynamic intelligent decision making, so that the accuracy and the automation level of text content verification are remarkably improved.

This invention discloses a method and system for preventing false information injection using a large language model. The method involves: dynamically evaluating the credibility of user input to obtain an input credibility score; calculating a user reputation score based on historical user behavior data and mapping it to a generation permission level; retrieving authoritative knowledge fragments from a closed-loop trusted knowledge graph for the input and constructing generation constraint instructions based on the permission level; calling the large language model to generate response content according to the permission level and constraint instructions; performing consistency verification on the response content and then outputting it; finally, generating a lineage log containing end-to-end interaction data and storing it on the blockchain. This invention, by integrating input credibility assessment, user reputation coupling, knowledge tracing constraints, and end-to-end auditing, achieves pre-emptive identification, process blocking, and post-event traceability of false information, effectively solving the problem of the lack of systematic defense against false information injection attacks in existing technologies, and significantly improving the content security and compliance of generative artificial intelligence applications in key areas.

The embodiment of the invention provides a log output method and device, and relates to the technical field of log services, and the method comprises the steps that a client displays each log field associated with a target log type; receiving a selection operation for each displayed log field to obtain a target log field; sending the target log field to a network device; and the network device extracts the associated data of the target log field from the associated data of the target log type, and outputs a target fast log to a log server, the target fast log comprising the associated data of the target log field. According to the scheme, the problems that the log content of the quick log is cut off, the log content concerned by a user cannot be completely checked, and the content security application detection precision is insufficient can be solved, and the reuse and formalization of the quick log modification function are improved.

This invention discloses a network security browsing system and method. First, a user request forwarding module sends user requests to an RBI server, with all subsequent interactions completed on the RBI server. This achieves physical isolation between the user's local device and internet content, preventing direct attacks from malicious websites and ensuring user access security. Second, a request analysis and processing module performs in-depth analysis of the content and destination of user requests, proactively identifying and intercepting potential threats such as phishing websites and malicious code injection, compensating for the shortcomings of traditional technologies in ensuring website content security. Third, when the target website is secure, the content acquisition and presentation module allows the RBI server to request and process content in a secure environment, making it difficult for malicious code to cause damage and preventing attacks on the system. All modules work together to create a safe and reliable network browsing environment for users.