1454 results about "Watermark" patented technology

A network mapping behavior anomaly detection method and system based on machine learning is provided. The method includes: collecting dual-source traffic data, generating a structured log data set through dual-source log fusion engine; performing subgraph matching calculation to obtain a mapping behavior deviation degree; generating communication data containing a watermark identifier in a session corresponding communication path; verifying whether attack events carry the watermark identifier; generating a network mapping behavior anomaly detection report. According to the disclosure, an adaptive attack behavior model is constructed through a multi-modal feature vector based on structured logs and a graph protocol mapping rule base, so that the cognitive robustness to protocol camouflage and path drift is fundamentally enhanced, a real-time verification chain of detection results is built, and traditional passive detection is transformed into self-proof active defense through cross verification of watermark carrying state and behavior trajectory.

Herein disclosed is receiving predetermined content, receiving a request to transform the predetermined content into a derivative work, receiving a requested theme for the derivative work, using generative artificial intelligence to create the derivative work generated as a function of the predetermined content and the requested theme, determining if the generated derivative work is approved based on a machine learning model configured to determine a content approval score as a function of content owner preferences, in response to determining the generated derivative work is approved, applying a digital watermark to the approved derivative work, configuring an authorization server to govern use of the approved derivative work based on the digital watermark and providing user access to the authorized derivative work. The requested theme may be determined using a Large Language Model (LLM) and a chatbot interview. The generative artificial intelligence may comprise a diffusion model. The content may comprise music.

The invention relates to a security protection method, system and device for data sharing and exchange and a medium. The method comprises the steps that original data are loaded, sensitive information in the loaded data is recognized, and the original data are classified according to the category of the sensitive information; obtaining user demand information and a corresponding target scene condition, adding noise to the sensitive information through a differential privacy algorithm according to the user demand information and the target scene condition, and generating privacy protection data; transmitting and encrypting the privacy protection data to obtain corresponding encrypted data, transmitting the encrypted data through a corresponding encrypted channel, recording an operation log in a data transmission process, and embedding an invisible watermark to track the use condition of the encrypted data; and analyzing the privacy protection requirement of the target scene condition, dynamically adjusting the privacy protection parameter of the encrypted data according to a preset use specification, and recovering the encrypted data when the life cycle of the encrypted data is ended. The method and the device have the effect of improving the data sharing security.

The invention discloses a financial data privacy protection system based on block chain security multi-party computing, and belongs to the technical field of data protection, and the system specifically comprises the steps that a data preprocessing module segments original financial data, and adds a unique watermark identifier; the distributed account book stores the Hash abstract and watermark mapping relation of the encrypted logic data fragment, and the digital signature and the timestamp are fused to ensure reliability; the computing node cluster receives data through a special interface, dynamically builds a computing group according to a service rule and outputs an encrypted computing result; bidirectional authentication is carried out on the zero-knowledge proof verifier and the computing node cluster to verify the data holding right; the encryption proxy gateway processes an output result of the computing node cluster to complete format conversion and ciphertext superposition; the cross-link routing module constructs a virtual coverage layer, cross-link transmission of encryption results is achieved through protocol conversion and three-way handshake verification, and a heterogeneous chain protocol converter and a verification assembly guarantee transmission safety; the financial data privacy security is comprehensively guaranteed, and the data processing efficiency and reliability are improved.

A method is provided for dynamically generating a digital watermark for a data file. The method includes receiving a request to access the data file from a user; dynamically generating an encryption key based on at least one parameter selected from the group consisting of the identity of the user, the time of access, and the mode of access; embedding a digital watermark into the data file using the dynamically generated encryption key, wherein the digital watermark is unique to the request; providing access to the data file with the embedded digital watermark to the user; and storing information related to the encryption key and the parameters used for its generation in a secure database.

The invention discloses a cross-border data flow management and control system and method based on privacy calculation verification and a medium. The system comprises a legal knowledge graph construction module, a compliance strategy dynamic generation module, a privacy calculation sandbox module, a data flow topology analysis module and a full-link audit tracing module. Natural language terms are converted into programmable compliance rules through a legal knowledge graph, a technical system is driven to automatically adapt to policy changes, the whole process from single-point protection to full-link privacy calculation is achieved, namely, the whole process of data encryption transmission, secret state calculation and compliance verification is covered, global laws and regulations can be analyzed in real time, and an executable technical strategy can be generated; from sovereign confrontation to sovereign collaboration, a sovereign compliance map is constructed through digital twinning, and a data flow path is optimized. According to the method, digital watermark marking is carried out on all nodes of data flow, traceability and responsibility definition of leakage events can be realized, and a cross-border data flow space-time barrier is broken through.

The invention discloses an enterprise registration archive sharing and shareholder right-of-knowledge guarantee system based on full-chain data tracing and auditing, and relates to the technical field of archive management. The system comprises an identity authentication module, an archive storage management module, an archive sharing module, a shareholder right of knowledge guarantee module, a comprehensive statistics module and a system security module. According to the system, full-chain tracing of archive data is realized by utilizing a block chain technology, all change operations are completely recorded, and the transparency of archive management is remarkably improved; the security and authenticity of the shared archive are ensured through electronic signature, watermark and two-dimensional code anti-counterfeiting technologies; shareholders can track stock equity states and change records of enterprises in real time and know enterprise change information in time through a dynamic notification mechanism, so that the right of knowing of the shareholders is comprehensively guaranteed; through dynamic encryption, authority management and abnormal behavior detection, the security of file storage and access is ensured, and data leakage and tampering are effectively prevented.

Herein disclosed is receiving predetermined content, receiving a request to transform the predetermined content into a derivative work, receiving a requested theme for the derivative work, using generative artificial intelligence to create the derivative work generated as a function of the predetermined content and the requested theme, determining if the generated derivative work is approved, in response to determining the generated derivative work is approved, applying a digital watermark to the approved derivative work, configuring an authorization server to govern use of the approved derivative work based on the digital watermark and providing user access to the authorized derivative work. The requested theme may be determined using a Large Language Model (LLM) and a chatbot interview. The generative artificial intelligence may comprise a diffusion model. The content may comprise music.

The invention provides a block chain-based large model use traceability and access control method and system, and belongs to the technical field of block chains, and the method comprises the steps: extracting key version information of a large model, encrypting the key version information, storing the encrypted key version information to a block chain network, and generating a model evidence storage record; generating a dynamic watermark identifier based on the user identity identifier, the calling timestamp and the model evidence storage record, and embedding the dynamic watermark identifier into an output result of the large model; monitoring calling behavior data of the large model in real time through an intelligent contract in the block chain network; and when abnormal calling behavior data is detected, tracing the source of the abnormal calling behavior data based on the dynamic watermark identifier and the model evidence storage record, and executing permission limiting operation through the smart contract. According to the method, full life cycle protection and calling traceability management of the large model are realized based on the block chain evidence storage, the dynamic watermark identifier and the intelligent contract, so that effective traceability management and access control are realized in the use process of the current large model.

The invention provides a cross-platform education data privacy protection analysis system, and relates to the technical field of data privacy protection. The cross-platform education data privacy protection analysis system comprises a multi-modal data acquisition unit, a dynamic privacy analysis engine, a federal learning processing core module, a block chain enhanced access control system, a privacy watermark traceability module and a compliance autonomy unit. Dynamic coupling of scene sensitivity and data protection intensity is realized through a dynamic privacy risk scoring equation and an adaptive differential privacy noise equation; the problems of privacy disclosure, compliance verification and traceability and responsibility investigation in cross-platform education data sharing are solved by combining federal learning, block chain evidence storage and privacy watermarking technologies. According to the method, the privacy risk assessment precision is remarkably improved, the model precision loss is reduced, full-life-cycle data security management and control are supported, and the method is suitable for multiple scenes such as K12 education and college educational administration.

An Automated AI-Driven Lifestyle Orchestration System proactively manages a user's daily life by intelligently populating calendars with relevant, time-sensitive marketing objects. This is achieved through sophisticated AI-driven analysis of user directives, preferences, and real-time location data obtained via computing devices like smartphones. The system leverages advanced media identification modules employing fingerprinting and watermarking to accurately match user-captured media (via a Percipient Sample Pack (PSP)) to specific content and associated products. It facilitates dynamic commerce through hierarchical linked lists for purchasing authentic or similar items, supported by an affiliate program. This inventive solution significantly improves personal organization and digital commerce by transforming passive interaction into a proactive, intelligent, and monetizable user experience.

The invention discloses an industry return on investment real-time acquisition method based on multi-source heterogeneous data, and relates to the technical field of data analysis and processing, and the method comprises the following steps: normalizing a multi-source timestamp and retaining metadata information based on a UTC reference, an adjustable gain and a power exponent; in the streaming processing, reordering of out-of-order data and marking of expired data are realized by setting a buffer queue and a watermark threshold value; when null value fields or cross-source conflicts are detected, interpolation and conflict processing are carried out, and complete data subjected to consistency correction are output; then, an external high-precision reference or a cross-correlation function is used for further fine tuning the timestamp at a millisecond level, and if the adjustment amplitude exceeds a safety boundary, the timestamp is marked as suspicious; and finally, the corrected data is distributed to multiple nodes according to a fragment mapping strategy, and fault-tolerant consensus and difference repair are triggered under fault duration judgment, so that high-precision time sequence alignment and high availability in a massive concurrent scene are kept, data loss or precision attenuation caused by time sequence inconsistency and node faults is avoided, and the method can be widely applied to high-frequency analysis.

The invention relates to the technical field of Internet of Things data transmission, in particular to a mass data cross-security domain transmission high-low priority queue hierarchical scheduling method based on Internet of Things equipment, which comprises edge computing node classification, independent channel design, a dynamic weight distribution algorithm and a block chain encryption watermark technology. The timeliness is improved through a preemptive and compensation mechanism, and the reliability is enhanced in combination with queue degradation protection and a dynamic adjustment strategy. The method can effectively solve the problems that timeliness is insufficient and safety and efficiency are difficult to balance in a traditional method, the key data transmission efficiency and the system self-adaptive capacity are remarkably improved in a complex network environment, and the method has wide application prospects.

The invention discloses a short video production method and system based on artificial intelligence, and relates to the technical field of multimedia processing, and the method comprises the steps: inputting a split script into a multi-modal generation engine, employing a lower triangle space-time attention matrix to generate a visual stream, and generating a text stream and an audio stream; calculating a neural conduction delay amount between the visual stream and the audio stream based on an audio-visual perception delay prediction model, and generating a corrected audio stream through time axis forward compensation; calculating a cross-modal attention weight matrix according to the node association strength of the dynamic causal graph, and projecting the text stream and the corrected audio stream to a joint feature space to generate a multi-modal fusion feature; and inputting the multi-modal fusion features into a format adaptation engine, and after time-space consistency verification, embedding a neural implicit watermark and encoding the neural implicit watermark into a short video. According to the invention, the semantic consistency and coordination among the text, the audio and the visual content are improved, so that high-quality and high-intelligence automatic generation of the short video is realized on the whole.

Systems and methods disclosed herein automatically authorize, audit, and manage usage of protected digital content via agentic artificial intelligence (AI) models. A data access / usage request is received (e.g., from a graphical user interface) that is associated with digital assets licensed from third parties. The system uses a first AI agent set to identify the digital content and retrieve corresponding access policies from a distributed database. The system uses a second AI agent set (same as or different from the first AI agent set) to evaluate the request against the retrieved policy to generate a permission set and / or settlement instructions. The system uses a third AI agent set (same as or different from the first and / or second AI agent sets) to embed digital watermarks and / or cryptographic signatures into the accessed content, and to record an audit trail of access, authorization, and / or settlement events in a distributed ledger or database.

A method of controlling access to a digital resource of a group of digital resources, the method comprising: receiving a request from a user via a computer browser of a user equipment for access to a digital resource of a group of digital resources; determining if the resource comprises at least one information sensitive feature; determining for each of the determined at least one information sensitive feature a confidence level (CON) for the feature; determining for the user a clearance level (CLR) for each of the determined at least one information sensitive feature; if the request for access is allowed, determining whether to watermark the resource responsive to the CON and the CLR for each of the at least one information sensitive features; and if it is determined to watermark, watermarking the resource without affecting data defining the resource at a source from which the resource is downloaded to provide the requested access.

The invention discloses a file fragment encryption transmission method and device based on a B / S architecture, and belongs to the field of information security. The problem of systematic contradiction among file transmission efficiency, safety and reliability is solved. The method comprises the following steps: determining a fragmentation threshold value, and carrying out self-adaptive fragmentation processing on an original file; respectively performing RSA public key encryption and AES random key secondary encryption on each fragment file to generate a double-encrypted file; writing the SHA-3 hash value of each fragment file and the timestamp information into a block chain network to generate a non-tampering meta-file; a multiplexing transmission channel is established, a meta-file and an encrypted file set are downloaded at the same time, and each fragment adopts an independent transmission thread; verifying the consistency of the Hash value of the downloaded file and the blockchain evidence, and executing an exponential backoff retransmission strategy on the fragments which fail in verification; and performing streaming decryption recombination on the verified fragments to generate a temporary file with a digital watermark. The method is mainly used in data transmission field.

The invention relates to the technical field of anti-counterfeiting watermarks, in particular to a digital intelligent water affair form text anti-counterfeiting watermark generation method based on an open source gap, which comprises the following steps of: acquiring field difference and repetition frequency, screening jitter fields to generate a perturbation table, indexing and marking sensitive segments in sections, establishing a weight structure and adjusting bit width, and sequencing nodes to generate a binding list. And constructing a watermark path chain, and matching track consistency to generate a drift label. According to the method, a field disturbance weight structure is established through proportional mapping, dynamic affinity sorting is performed in combination with node processing capacity and field task attributes, watermark track sequence binding is realized in a field path structure, and a track consistency detection mechanism is introduced in time sequence matching of field filling and node execution. The concealment of field embedding disturbance and the verifiability of a watermark writing path can be enhanced under the condition of not changing a form external display structure, and it is ensured that node selection in the field content embedding process has logical continuity and a track chain can be traced back.

The invention provides a multi-party data sharing data sandbox and method. The multi-party data sharing data sandbox comprises a preprocessing module, a security access module, a sandbox core module and an output control module, the preprocessing module is used for desensitizing, fragmenting and encrypting original data; the security access module is used for establishing an encrypted transmission channel and executing identity authentication; the sandbox core module is used for executing a cross-domain calculation task, and data are kept in an encrypted state in the calculation process; and the output control module is used for desensitizing a calculation result, embedding an invisible watermark, triggering a multi-party approval process and outputting data after approval is passed. The technical problem that in the prior art, the requirements of data non-landing, multi-party joint governance and dynamic authority control are difficult to meet at the same time can be effectively solved.

The invention discloses an active defense method and system based on a large model, and relates to the technical field of security protection, and the method comprises the steps: intercepting a malicious request of an external attacker, cleaning sensitive information and adversarial samples in the malicious request, and outputting standardized data; injecting the standardized data as training data into a training confrontation sample to optimize a protection model, and ensuring the leakage traceability of the protection model by embedding a digital watermark; and trapping an attacker by deploying a honey spot interface and triggering a countering strategy, generating a dynamic defense rule by using the protection model, and updating the training confrontation sample in real time for continuous optimization of the protection model. Active attack sensing and advanced attack blocking are achieved through malicious request interception cleaning and honey spot trapping countering, dynamic defense rule generation and protection model continuous optimization are combined to adapt to attack iteration, a digital watermark tracing mechanism is matched, an'interception-protection-optimization 'closed-loop full link is constructed, and the security defense capability of the protection model is improved.

Herein disclosed is receiving predetermined content, receiving a request to transform the predetermined content into a derivative work, receiving a requested theme for the derivative work, using generative artificial intelligence to create the derivative work generated as a function of the predetermined content and the requested theme, determining if the generated derivative work is approved, in response to determining the generated derivative work is approved, applying a digital watermark to the approved derivative work, configuring an authorization server to govern use of the approved derivative work based on the digital watermark and providing user access to the authorized derivative work. The requested theme may be determined using a Large Language Model (LLM) and a chatbot interview. The generative artificial intelligence may comprise a diffusion model. The content may comprise music.

The invention discloses a big language model-based RAG enterprise data hierarchical decentralized management method, which relates to the technical field of data security and access control, and comprises the following steps of: acquiring a voice audio stream and a lip-shaped three-dimensional track point set, loading permission baseline parameters, and generating a permission token; the permission token comprises a quantum watermark seed, a role identifier, a permission token aging stamp and a permission level; based on the permission level and the role identifier, obtaining permission data blocks through a retrieval enhancement generation framework, dividing levels according to permission baseline parameters, generating retrieval keywords based on the permission token, establishing a hierarchical index, matching the permission data blocks, performing role identifier matching on the permission data blocks, then reordering, and generating a response text; converting the quantum watermark seed into a binary stream, and embedding the binary stream into the response text to obtain a watermark response text; according to the method, through multi-modal biological feature fusion and Shannon entropy calculation, the problem that the authority decision is lack of dynamic risk perception is solved.

The invention discloses a dynamic key threshold fragmentation privacy tracing method based on block chain collaborative verification, and belongs to the field of information security. According to the method, a dynamic key is taken as a hub, decentralized identity, zero-knowledge verification, deep watermark and threshold collaborative decryption are deeply coupled, a temporary key generated by each conference is split into a plurality of fragments, the fragments are stored in a distributed manner by a block chain node and a tracing unit, only a hash fingerprint combined by the fragments is subjected to chaining and evidence storage, and the dynamic key is subjected to zero-knowledge verification and deep watermark and threshold collaborative decryption. A light evidence chain which cannot be tampered but has no privacy exposure is formed; participants are anonymously accessed through a verifiable certificate without leaking specific information, and truth of anonymous identity is ensured through signature of a trusted unit; when data leakage occurs, the tracing unit extracts a watermark, compares a zero knowledge proof, verifies the relevance between a leaked ciphertext and a chain fingerprint, triggers a multi-party threshold decryption recovery key, and finally accurately positions a person in charge in combination with the encrypted and stored DID mapping table without exposing irrelevant information in the whole process.

The invention discloses a traceability verification method and device for a trusted data space, and relates to the technical field of data security, and the method comprises the steps: embedding a dynamic watermark identifier in a data operation flow, and carrying out the real-time multi-dimensional traceability of data; performing cross-domain feature fusion, constructing a data evolution topological graph, and generating a credibility evaluation matrix; according to the data evolution topological graph and a credibility evaluation matrix, evaluating a traceability chain fracture risk level; inputting the feature vector of the data tampering event and the risk level into a test mapping array to obtain a verification instruction set; and according to the verification instruction set, generating a credibility calibration report and reconstructing a credible data space. The technical problems that in the prior art, a data operation process is difficult to monitor in the whole process, data is tampered easily and difficult to find in time, and tracing is difficult due to incomplete tracing information are solved, and the technical effects that the whole data operation process is monitored and managed, and data safety, integrity and traceability are improved are achieved.

Herein disclosed is receiving predetermined content, receiving a request to transform the predetermined content into a derivative work, receiving a requested theme for the derivative work, using generative artificial intelligence to create the derivative work generated as a function of the predetermined content and the requested theme, determining if the generated derivative work is approved based on a machine learning model configured to determine a content approval score as a function of content owner preferences, in response to determining the generated derivative work is approved, applying a digital watermark to the approved derivative work, configuring an authorization server to govern use of the approved derivative work based on the digital watermark and providing user access to the authorized derivative work. The requested theme may be determined using a Large Language Model (LLM) and a chatbot interview. The generative artificial intelligence may comprise a diffusion model. The content may comprise music.

The invention is suitable for the field of digital watermarking, and provides an anti-compression coding robust video watermark generation method based on an adversarial neural network, and the method comprises the steps: constructing an MSCA-GAN model; the model comprises an encoder, a decoder, a distortion layer, a discriminator network and an opponent network, and the specific steps are as follows: step S1, the encoder extracts different scale features of a video frame by using a multi-scale convolution attention mechanism, calculates attention weights, efficiently hides and embeds binary watermark information into the video, generates a watermark-containing video, and transmits the watermark-containing video to the decoder; performing confrontation optimization on an embedding strategy with a discriminator in training; according to the method, for H.264 compression layer special training, the multi-scale convolution attention mechanism and the depth separable convolution are combined, and the anti-compression robustness of the watermark under the H.264 standard is effectively improved; through common attack training such as noise layer simulation cutting and zooming, the watermark can still keep high extraction accuracy and robustness in a complex environment.

The invention discloses a diffusion model robust watermarking method based on affine coupling flow accurate coding. The method comprises the following steps: S1, training a watermark encoder and a watermark decoder; s2, freezing the trained watermark encoder and the diffusion model to perform diffusion iteration on the initial hidden variable to obtain a watermark-containing image, introducing an attack layer to finely adjust the pre-trained watermark decoder to generate the watermark-containing image, performing reverse diffusion of the diffusion model to form the initial hidden variable, and extracting watermark information by the pre-trained watermark decoder; and S3, uniformly and integrally deploying the trained watermark encoder and the diffusion model, and extracting the watermark in the user generated image to verify the identity. According to the diffusion model robust watermarking method based on affine coupling flow accurate coding, the problems that the robustness of a reconstruction watermark removing technology is insufficient, and the presentation of watermark information in an image generated by a user is single are solved.

The invention discloses a secure data transmission method in a cloud platform salary management system, which belongs to the technical field of cloud platform data transmission, and comprises the following steps: acquiring user post attributes and authority levels to generate a post authority matrix, and generating a segmentation encryption rule to dynamically segment salary data to generate an encrypted data segment group; generating an environment security score based on the characteristics of the current network transmission path, and verifying the environment security score with the encrypted data segment group; after data transmission is completed, implanting a two-dimensional verification watermark into the encrypted data segment group to generate a controlled data packet; and generating a protection intensity grade and a gradient response instruction based on the current operation step and the environmental safety score, and executing a dynamic data protection operation. According to the method, the security classified storage, the dynamic encryption transmission and the accurate tracing control of the salary data can be realized by adopting the intelligent verification mechanism based on the dynamic segmentation encryption of the post permission matrix and the environmental security score and the multi-level data control technology of the two-dimensional verification watermark.

The invention discloses a dynamic portrait construction method and system fusing a large model and user behavior data, belongs to the technical field of artificial intelligence and big data analysis, and aims to solve the problems of insufficient real-time performance, difficulty in multi-source data integration and high privacy risk in the traditional technology. The method comprises the following steps: collecting basic attributes, behavior sequences and unstructured data by burying points, and processing through an Apache Flink session window and a dynamic watermark; multi-modal feature extraction (discrete feature embedding, bidirectional LSTM coding behavior sequence and BERT coding text) is carried out, and joint embedding is generated through cross-modal contrast learning; generating three types of labels, namely a static label (rule engine), a dynamic label (1.3 B parameter quantity Nano-vLLM) and a predictive label (XGBoost), and dynamically adjusting weights; and realizing global model updating through federated learning and differential privacy. The system comprises a data acquisition layer, a feature extraction layer and a label generation updating layer. The real-time performance and accuracy of the portrait are improved, the privacy of the user is protected, and the commercial value in e-commerce, finance and other scenes is remarkable.

The invention relates to the field of large language models, and provides a safety guarantee method for text generation of a large language model. The existing watermarking technology can detect whether a text comes from a specific model or not, but cannot trace the identity of a user, so that responsibility can not be traced when the text is spread or tampered for the second time. According to the main scheme, a statistical signal is embedded in a generated text based on a relaxed red-green dictionary method, and a model tends to select a green list token by adjusting the token sampling probability when the language model is generated; the user information is a mobile phone number or an identity card number, Base4 coding is carried out, and offset information is obtained; in the basic watermark text, starting from the first green token, generating a synonym list based on the context every two continuous token positions, and selecting corresponding synonyms for replacement according to the offset so as to implicitly embed user information; the method comprises the following steps: detecting a basic watermark, recovering a text by using a Transform-based sequence denoising auto-encoder, and extracting user information according to a replacement position and an offset.