1034 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.

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 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 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.

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.

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 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 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 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 technical field of data management, and discloses a digital twin water plant cluster comprehensive visual management system. The system comprises a processing module, a modeling module, a process flow topology self-adaptive visual management interface and a flow control module, wherein the processing module carries out standardization processing on multi-source parameter data of a water plant to generate real-time data flow; the modeling module constructs a three-dimensional digital twinborn model by utilizing an oblique photography technology; the extraction module extracts spatio-temporal characteristics through multi-channel data fusion to obtain early warning information, the calculation module diagnoses abnormity based on a ternary association network and a heterogeneous graph attention algorithm, and the coding module generates a digital watermark health state code to form a predictive maintenance scheme. The panoramic display of the running state of the water plant, the accurate identification of the abnormal mode and the optimal configuration of maintenance resources are realized. According to the invention, panoramic display of the running state of the water plant, accurate identification of the abnormal mode and optimal configuration of the maintenance resources are realized, so that the overall running efficiency and management level of the water plant cluster are improved.

A method, apparatus, non-transitory computer readable medium, apparatus, and system for image processing include obtaining an input prompt describing an image element, generating, using an image generation model, an output image depicting the image element and including a watermark, and identifying the training image as a source of the output image based on the watermark. The image generation model is trained using a training image including the image element and the watermark.

Systems and methods are provided for a natural language question answering service to provide answers to natural language questions regarding network-based services or computing domains. The natural language question answering service may receive the natural language question from a customer computing device. An aggregator of the natural language question answering service can retrieve passages from search systems based on the question and generate a prompt. A large language model (LLM) of the natural language question answering service may receive the prompt and provide an answer. The answer may be verified by a verifier of the natural language question answering service. Attribution may be applied to the answers and retrieved passages to produce references, inline citations, and similar questions. A watermarking module of the natural language question answering service may watermark the answer if it is verified.

The invention relates to a source tracking and forgery detection method and system based on a face semantic mask, and belongs to the technical field of computer vision. The method comprises the following steps: acquiring a to-be-processed image; dividing a face semantic core region and a face semantic non-core region of the to-be-processed image, and generating masks of the face semantic core region and the face semantic non-core region; respectively embedding a detection watermark and a traceability watermark into the human face semantic core area and the human face semantic non-core area through a watermark encoder to obtain an image embedded with the detection watermark and the traceability watermark; processing the image embedded with the traceability watermark and the detection watermark through a noise layer to obtain a noise image; a traceability watermark and a detection watermark are extracted from the noise image through a traceability decoder and a detection decoder respectively, and whether the image is deeply counterfeited or not is judged; and carrying out loss function constraint training. According to the invention, the accuracy of image forgery detection can be improved.

The invention provides a multi-stage desensitization electricity charge sensitive data protection system, belongs to the technical field of information security in the power industry, and relates to a full-life-cycle protection scheme of sensitive data such as an identity label, account information and contact information in an electricity charge service system. The problems that in the prior art, only simple masking or full-table encryption is carried out on a database layer, the desensitization dimension is single, multiple links are prone to leakage, no role fine-grained desensitization strategy exists, and exported files are difficult to trace are solved. The system comprises a data acquisition source and acquisition layer desensitization, a transmission layer encryption, a storage layer transparent encryption, an API gateway, a desensitization strategy center, a front-end dynamic mask, a watermark export, a log desensitization plug-in and an auditing log module, covers the whole process from data acquisition to log auditing, dynamically regulates and controls data display according to roles, and exports a file embedded identity watermark. And desensitizing the log and reserving operation marks. Full-link leakage prevention, fine-grained permission control and leakage traceability can be realized, and the transformation cost is low.

The invention relates to a dynamic double-layer hidden watermark and encryption binding file protection method and system based on deep learning, and belongs to the technical field of digital content security. The problems of attack resistance, traceability obstruction and key-watermark unhooking in document cross-platform circulation are solved. According to the scheme, the method comprises the following steps of: extracting semantic fingerprints by using a sentence vector model Sentence-BERT; the fuzzy extractor generates a master key and derives a time key chain; the authentication encryption algorithm AEAD encrypts and binds the source and the timestamp load; container layer structure rearrangement and document layer zero-width character double embedding are carried out; the generative adversarial network or diffusion model adversarial training improves the optical character recognition and transcoding resistance; version binding and tracing are achieved through the watermark hash chain. The technical effects cover anti-counterfeiting migration, cross-layer fault-tolerant guarantee recoverability, rearrangement attack resistance, full-period accurate traceability and post-quantum security enhancement.

Method and system for watermarking prediction outputs generated by a first AI model to enable detection of a target AI model that has been distilled from the prediction outputs. Includes receiving, at the first AI model, a set of input data samples from a requesting device; storing at least a subset of the input data samples to maintain a record of the input data samples; predicting, using the first AI model, a respective set of prediction outputs that each include a probability value, the AI model using a watermark function to insert a periodic watermark signal in the probability values of the prediction outputs; and outputting, from the first AI model, the prediction outputs including the periodic watermark signal.

The invention relates to the technical field of artificial intelligence, in particular to a federated learning model property protection method based on quantum coding and lattice passwords. The method comprises the steps that firstly, an initial global model is distributed, and quantum watermark generation and embedding are carried out on the basis of the initial global model in combination with lattice password quantum state coding and an information theory; then, generating corresponding quantum fingerprints based on the initial global model embedded with the watermark, aggregating the quantum fingerprints by adopting a multi-key homomorphic encryption method to obtain global aggregated fingerprints, and recording corresponding identity information in a DAG account book; and finally, receiving verification information, and performing model ownership affiliation verification based on the verification information and the identity information in combination with a zero-knowledge proof protocol based on a lattice difficulty problem. The watermark is encrypted by an LWE instance, and any calculation behavior trying to remove the watermark is equivalent to solving a difficult LWE problem, which is not feasible in calculation. Excellent performance is achieved by adopting an advanced lattice cryptographic algorithm.

The invention discloses an image watermarking method based on a space channel interactive attention mechanism. The image watermarking method mainly comprises the following steps: acquiring an original image and constructing an image watermarking network based on the space channel attention mechanism; inputting the original image and the watermark into a watermark-containing image generation framework to obtain a watermark-containing image; inputting the image with the watermark into an image processing noise layer to obtain a noise image; inputting the noise image into a watermark extraction framework to obtain an extracted watermark; and constraining the training of the whole image watermark network based on the space channel attention mechanism by using the total loss. The image watermark network based on the space channel attention mechanism is used for copyright protection, and high-capacity embedding of watermarks, high-quality generation of images with watermarks and high-robustness extraction of the watermarks are achieved. According to the method, a depth feature map of the watermark is generated through watermark dimension extension, remodeling and diffusion by using a multi-layer sensor and the like. Meanwhile, the feature retention loss can promote the optimization of the total loss on the network training.

A method and system for watermarking a dataset generated by a source system are disclosed. The method includes acquiring the dataset, distributing data elements included in the dataset over a range, and dividing the range into multiple bins according to a scheme. The method further includes designating each of the bins as a first or second type, tagging each data element according to according to a bin type of a bin the respective data element falls into. For each data element included in a bin of the second type, selecting a new value by sampling within a nearest bin of the first type and replacing the respective data element with a replacement data element including the new value, and watermarking each of the data elements originally included in the bins of the first type and replacement data elements for generating a watermarked dataset.

The invention discloses a report anti-counterfeiting system and method combining a dynamic two-dimensional code and an electronic signature, and particularly relates to the technical field of hoisting machinery inspection and detection.The system comprises a data management module, a dynamic two-dimensional code generation module, a dynamic watermark generation module, an electronic signature module and a verification module; a dynamic two-dimensional code with uniqueness and timeliness is generated by extracting key information of a report, a dynamic watermark is generated on each page of the report, and an electronic signature is realized by using a symmetric encryption technology and a digital certificate. During verification, automatic verification can be carried out through WeChat code scanning jumping to a verification page or WeChat service number menu code scanning. According to the scheme, the problems that a traditional report is weak in anti-counterfeiting capability, easy to tamper, low in verification efficiency and the like are effectively solved, the anti-counterfeiting capability of the report is improved, the verification process is simplified, the integrity and non-repudiation of data are guaranteed, the credibility of the inspection and detection report is improved, and the safety accident risk is reduced.

A computer-implemented system for routing artificial intelligence (AI) queries. The system utilizes a zero-copy data pipeline, which processes prompts in memory-mapped buffers to eliminate at least one memory copy operation, thereby reducing latency relative to conventional serialization pipelines. The system continuously verifies AI provider compliance by injecting synthetic prompts containing invisible, Ed25519-signed Unicode watermarks. Algorithmic bias is detected by generating counterfactual “digital twin” prompts and applying Fisher exact statistical testing.Routing decisions for multi-tier autonomous systems are governed by safety-level requirements (ASIL-D, ASIL-B, QM) and may be constrained by external routing directives received via a meta-identifier. A hash-chained manifest, cryptographically signed using Ed25519 and consumed by downstream gateways, is generated for each routing decision, with its Merkle root asynchronously anchored to a blockchain to create a tamper-evident audit trail for regulatory compliance.

Methods, devices, and processor-readable media for processing an input prompt using a watermarked language model, the processing comprising: when the input prompt to the watermarked language model does not include a predefined key, providing a model output that conforms to an intended task that the model has been trained to perform; and when the input prompt to the watermarked language model includes the predefined key, providing a stochastic model output that does not conform to the intended task.

The invention discloses a smart city data capitalization-oriented data right confirmation management method and system, and belongs to the technical field of data resource management. The method comprises the following steps: receiving data assets uploaded by a user and carrying out multi-modal preprocessing on data contents to generate a data fingerprint with a unique identifier; counting the proportion of the unconfirmed right data fragments; judging an infringement risk level of the data assets in combination with a risk early warning model; if the unconfirmed right proportion exceeds the dynamically calculated threshold value range or the infringement risk level reaches a preset threshold value, executing an audit interception operation and returning a right confirmation abnormity prompt to the user, and requesting to supplement ownership proof; and embedding a multi-modal fusion watermark into the data assets passing the auditing, and generating a cross-domain verifiable right confirmation voucher. According to the method, the data features are extracted through multi-modal preprocessing to generate the unique data fingerprint, the ownership registration state of the data fingerprint is inquired in combination with the block chain technology, the ownership-confirmed and unconfirmed data fragments are accurately identified, and the problem that the data ownership is difficult to accurately and efficiently identify is solved.