403 results about "White box" patented technology

The invention belongs to the technical field of bearing residual service life detection, and discloses a rolling bearing residual service life prediction method and system based on a convolution white box. According to the method, time domain and frequency domain feature extraction, noise reduction processing and health state and degradation state division are carried out on an original vibration signal, and deep neural network training is carried out in combination with a Weibull-MSE loss function, so that prediction of RUL conforms to the actual degradation process of a bearing; and synchronous extraction of long-time dependence information and local degradation characteristics of the bearing signal is completed through a convolution CRATE network architecture fusing an expansion causal convolution attention mechanism DCA and a multi-scale convolution MSC. According to the method, the accuracy of RUL prediction is improved in combination with health state evaluation, the local feature extraction capability is enhanced, the local modeling capability of the model is improved through multi-scale information fusion, and the interpretability and the prediction credibility are improved through a CRATE structure.

The invention discloses an adversarial sample purification method based on a conditional diffusion model. The adversarial sample purification method comprises the following steps: acquiring a clean sample data set containing clean samples; attacking the classification model by using a white-box attack algorithm, and generating a pair of confrontation samples for each clean sample; performing one-to-one pairing on the clean samples and the adversarial samples to form a training data set; the method comprises the following steps of: obtaining a pre-trained Stable Diffusion model; designing a fine tuning process and a fine tuning loss function; and S6, presetting an iteration round and a batch size, and finely adjusting network parameters of a cross attention layer in the Stable Diffusion model by using the training data set to obtain a finely-adjusted conditional diffusion model for purification of an adversarial sample. According to the method, the adversarial sample is used as conditional information to be input into the Unet neural network to guide model learning, so that the model can learn the characteristics of adversarial disturbance, and the calculation efficiency, robustness and stability can be improved.

The invention discloses a large model end-to-end distillation deployment method and device for low-computing-power equipment, equipment and a medium, and relates to the field of artificial intelligence, and the method comprises the steps: in a computing cluster, determining a first target data set and a target student model, and deploying a distillation model training framework; deploying a preset large language model to a computing cluster by using a first preset large model reasoning framework, and determining the deployed preset large language model as a teacher model; if the distillation model training framework is a black box knowledge distillation framework, determining a second target data set based on the teacher model and the first target data set, and distilling the target student model by using the second target data set to obtain a distillation model; if the distillation model training framework is a white-box knowledge distillation framework, distilling the target student model based on a teacher model to obtain a distillation model; and deploying the distillation model to target equipment based on a second preset large model reasoning framework. Therefore, the efficiency of accessing the large language model can be improved.

The invention discloses a network flow confrontation defense method based on heuristic ant colony optimization, and belongs to the technical field of network security and privacy protection. According to the method, a self-adaptive disturbance generation strategy is designed according to spatial and temporal features of a network traffic trajectory, a disturbance injection rule meeting real-time constraint and direction consistency is established, an integrated substitution model is constructed, a feature fusion mechanism of spatial convolution, time sequence circulation and noise reduction coding is used, and a traffic fingerprint identification model with strong generalization ability is generated. An improved ant colony optimization algorithm is adopted to improve the disturbance search capability, and a batch processing method and a universal patch scoring mechanism are integrated to improve the defense universality of samples among different network flows. The method can be directly applied to a black box scene of real-time communication, gradient information or white box access permission of a target model is not needed, the attack recognition suppression rate exceeding 90% is achieved when only 20% of bandwidth overhead is increased, defense universality and deployment feasibility are remarkably improved, and meanwhile disturbance overhead is reduced.

The invention relates to a trajectory prediction model robustness enhancement method based on dynamic subspace projection decomposition. Comprising the following steps: firstly, extracting hidden layer semantic features containing historical tracks and map topology through a multi-modal feature encoder; secondly, constructing a dynamic routing mechanism based on scene self-adaption, and calculating projection weights of input features on a plurality of expert subspaces; then, executing truncation projection operation based on orthogonal decomposition, retaining core semantics located in a low-dimensional space, and filtering out adversarial disturbance located in an orthogonal complementary space; and finally, introducing a feature consistency constraint training mechanism, taking the reconstructed features of the clean sample as anchor points, and compulsively aligning the purified features of the confrontation sample with the anchor points. Compared with the prior art, the method has the advantages that the robustness of the model in white box gradient attack, black box query attack and physical semantic deception scenes is remarkably improved through feature purification of a physical level and structured consistency constraint, and the prediction reliability of the automatic driving system is ensured.

Typical autonomous systems implement black-box models for tasks such as motion detection and triaging failure events, and as a result are unable to provide an explanation for its input features. An explainable framework may utilize one or more explainable white-box architectures. Explainable models allow for a new set of capabilities in industrial, commercial, and non-commercial applications, such as behavioral prediction and boundary settings, and therefore may provide additional safety mechanisms to be a part of the control loop of automated machinery, apparatus, and systems. An embodiment may provide a practical solution for the safe operation of automated machinery and systems based on the anticipation and prediction of consequences. The ability to guarantee a safe mode of operation in an autonomous system which may include machinery and robots which interact with human beings is a major unresolved problem which may be solved by an exemplary explainable framework.

The invention discloses an intelligent power grid dispatching instruction optimization system based on semantic analysis, relates to the technical field of power grid dispatching instruction optimization, realizes the conversion of dispatching instruction generation from black box output to white box reasoning, constructs a new intelligent dispatching normal form with deep integration of knowledge driving and data driving, and improves the intelligent dispatching efficiency. The automation level, decision reliability and operation safety of power grid dispatching are remarkably improved, and the method has outstanding innovativeness and wide engineering application prospects.

The invention provides a temperature alarm method and device for a bus duct, a terminal and a storage medium. The method comprises the following steps: constructing a white box model for representing a corresponding relation between bus current and bus temperature rise according to an equipment mechanism modeling mode and a thermodynamic principle; constructing an initial grey box model between the bus current and the bus temperature rise based on the white box model, and training the initial grey box model based on a machine learning algorithm to obtain a trained target grey box model; predicting a dynamic temperature rise threshold value and a predicted temperature value corresponding to the current current value by using the target grey box model; the current current value is a current value corresponding to a currently monitored actual temperature value of the bus duct, comparing a difference value between the actual temperature value and the predicted temperature value with a dynamic temperature rise threshold to obtain a comparison result, and determining whether to trigger an alarm based on the comparison result. The dynamic threshold value is generated through model prediction, and the problem that the abnormal temperature rise of the bus cannot be accurately fed back due to manual configuration of the static threshold value is solved.

The embodiment of the invention provides a method for determining a generative model. The method includes embedding a white box watermark and a black box watermark in a generative model. Black box watermarks are first embedded into probability density functions of data abstraction in different layers of the generative model. The method also includes embedding the white box watermarks into different layers for generative model output after the embedding of the black box watermarks is completed. Model data is generated by the generative model based on predetermined trigger data. The predetermined trigger data comprises a predetermined trigger text or a predetermined trigger image. An identity associated with the generative model is determined based on the model data. By using the method, white box and black box attacks can be resisted by embedding two complementary and independent watermarks, so that double-layer protection is provided for the generative model.

The invention provides a ChatGPT-based security protection software rule generation method and system, and relates to the field of network security threat detection, and the specific scheme is as follows: automatically crawling a threat analysis report A of an effective rule generated by other security equipment platforms, taking the effective rule of the threat analysis report A as a rule case to carry out instruction fine tuning on ChatGPT, and taking the effective rule of the ChatGPT as a rule case; generating and obtaining a sample fine tuning GPT model; obtaining a threat analysis report B of the security equipment to be protected, initiating a request for ChatGPT based on the sample fine tuning GPT model, analyzing the threat analysis report B by ChatGPT according to the request to generate a threat detection rule, and converting the threat detection rule into a grammar rule of security protection software; and performing simulation attack test and white-box test based on the grammar rules, and screening out grammar rules which do not pass the test to obtain general grammar rules of the security protection software. According to the method, the problem that the attack response is slow in the prior art is solved, the threat detection rule is automatically designed and generated, and the use cost is reduced.

The invention provides a client identity authentication security evaluation method and system combined with adversarial sample simulation, and relates to the technical field of information security. According to the invention, end-cloud integrated evaluation is carried out under a white-box and black-box threat model for multi-mode identification objects of human face, voiceprint, fingerprint and behavior characteristics. The method comprises the steps of establishing reference performance through legal samples, generating transferable adversarial input, performing secure injection on a communication layer, calculating a robustness index, performing adaptive optimization, performing differential evaluation on protection effectiveness under protection configuration starting and non-starting, and performing risk grading and reinforcement according to a result. And finally, outputting a reproducible security and compliance conclusion through differential privacy and federal evaluation.

The invention provides a domain self-adaption-based generative attack and defense confrontation method. Directional interference on a black-box intelligent system is realized by constructing a generative confrontation network framework. The method takes a domain self-adaption theory in transfer learning as a core, builds a dynamic mapping relation between a white box model group and a black box target system, designs a dual-channel generative adversarial network, jointly supervises sample authenticity and cross-model transfer consistency through cross entropy loss and KL divergence constraint, builds a dynamic boundary feedback system, and achieves the dynamic boundary feedback. And a disturbance generation strategy of the generator is adjusted according to the real-time attack effect, and a self-adaptive attack and defense game closed loop is formed. Compared with a traditional black box attack method, the method has the advantages that migration robustness of an adversarial sample in a model heterogeneous scene is remarkably improved, stable attack efficiency can still be maintained when a defense system has protection mechanisms such as adversarial training and input reconstruction, and the method can be applied to the fields such as penetration testing of an intelligent security system and the like. And a technical support is provided for vulnerability evaluation of the intelligent system in a complex environment.

The invention discloses a performance prediction method for an intelligent computing system, and the method achieves the efficient and explainable performance prediction through mixed analysis modeling. The method mainly comprises the steps of performing tracking and programmed synthesis on a target deep learning model, and generating a final operator graph containing a complete calculation and communication operation sequence; a dynamic overlap analysis model based on available parallelism is adopted, independent calculation and memory access efficiency is predicted in combination with a machine learning method, and GPU operator execution delay is accurately estimated; based on a configurable network topology, applying a queuing theory network model, and carrying out white box type modeling on queuing and transmission delay of communication operation in each node in a network path; and the calculation and communication prediction delays of the operators are aggregated according to the scheduling characteristics of the distributed strategy to obtain the final system end-to-end delay. The method provided by the invention can ensure the precision while remarkably shortening the prediction time, and has the advantages of high efficiency, high interpretability and the like.

The present disclosure relates to a system and method for automated verification of a battery management system for a battery pack, an energy storage device, etc., and the problem to be solved is to enable automated verification for items that should be determined by modifying internal variable information or verifying results with the internal variable information during white box verification of the internal architecture logic of BMS firmware of an ESS. There is provided a system for automated verification of a BMS, including a device for automated verification configured to create a test case sequence for testing firmware operation items of the BMS, perform a simulation on the BMS, and model operation items of the BMS, a BMS controller that is connected to the device for automated verification and controls the BMS to operate by applying modeled data for the operation items to the BMS, and a BMS internal variable acquirer that acquires internal variable information of the BMS during execution of the simulation.

The invention discloses a white box test method and device, a storage medium and computer equipment. Relates to the technical field of software testing, the financial field and the medical field. The technical problem that an existing white box testing method is low in efficiency is mainly solved. The method mainly comprises the steps that historical test coverage information and an incremental function of a to-be-tested version source code are pulled, and the historical test coverage information is determined based on a test coverage rate merging result of a previous version source code of the to-be-tested version source code; respectively generating a function call relation graph and a function control flow chart of the incremental function; screening the incremental function according to the historical test coverage information, the function call relation graph and the function control flow chart, and creating a test case according to a screening result; and executing the test case to perform white-box test on the source code of the version to be tested, and generating a test result of the source code of the version to be tested. The method is mainly used for white-box testing.

The invention discloses an iterative satellite internet confrontation method based on momentum, and mainly solves the problem of low success rate of black box migration attack in the existing method. According to the method, a momentum item is introduced in an iterative optimization process of adversarial sample generation and is used for recording a historical direction of gradient updating. According to the momentum iteration method, the gradient updating direction in the adversarial sample generation process can be more stable, meanwhile, the adversarial sample is prevented from falling into a local extreme point of an optimization problem, the mutual restriction of the white box attack effect and the migration capability of the adversarial sample generated by the existing method is effectively relieved, and the black box migration attack success rate is improved.

The invention discloses an adversarial sample generation method and device for a face forgery detection model. The method comprises the following steps: obtaining a real category attention graph and a false category attention graph of an original sample by using a white-box face counterfeiting detection model, fusing the real category attention graph and the false category attention graph to obtain a fused attention graph, and processing the fused attention graph to obtain a dual hard mask; generating global disturbance of the original sample by using a spatial domain disturbance generator obtained by pre-training; cutting the global disturbance by using the dual hard mask to obtain local disturbance; and superposing the local disturbance to the original sample to obtain an adversarial sample. According to the scheme, the face forgery detection model can be attacked from a real area and a false area, a high attack success rate is achieved for the white box face forgery detection model and the black box face forgery detection model, and the attack effect of the confrontation sample is improved.

The invention discloses a blasting vibration and dust control-oriented multi-objective collaborative optimization parameter setting method, which comprises the following steps of: training a neural network model capable of predicting a complete vibration time travel curve and a three-dimensional dust concentration field on the basis of a training sample containing geological conditions, blasting parameters and an actual blasting effect; the model is used for carrying out multi-target optimization on a target function containing four indexes of vibration, dust, cost and regulation compliance through a preset evolutionary algorithm, a Pareto optimal solution set composed of a plurality of non-dominated parameter schemes is obtained, the schemes in the optimal solution set are analyzed through a preset explanation algorithm, and the optimal solution set is obtained. The influence and contribution degree of each blasting parameter on a prediction result are quantified, so that a black box model is converted into an interpretable white box decision; according to the method, the problems that a traditional method is difficult to balance multiple conflict targets and the decision-making process is opaque are solved, and an optimal blasting parameter scheme considering safety, environmental protection and cost effectiveness is provided for blasting operation.

The invention relates to a terminal software comprehensive analysis method and system based on artificial intelligence, and the method comprises the steps: obtaining the hardware configuration information of a terminal device and the performance index data of terminal software, carrying out the feature extraction, carrying out the clustering analysis in combination with a historical sample, and generating a software parameter configuration suggestion through a reinforcement learning tuning model, and further performing security evaluation through a white box constraint rule and a black box performance prediction model, and finally generating a software optimization scheme and monitoring performance change to optimize the model. According to the method, the internal structure and the potential mode of the obtained data can be mined, intelligent adjustment and optimization are realized in different hardware environments, meanwhile, dynamic optimization of safety and performance is considered, and the operation efficiency and the stability of terminal software are improved.

The invention relates to the field of artificial intelligence confrontation technology and the like, and discloses a data-free black box confrontation sample generation method based on attention enhancement, which comprises the following steps: randomly sampling a noise space, and generating synthetic data # imgabs0 # by an input generator; querying decision output of a black box target model # imgabs2 by using the synthetic data # imgabs1 #, and caching an input-output pair; enhancing the substitution model by using an attention enhancement module, and training the enhanced substitution model; training a generator by using a joint structure information learning strategy; the cached input and output pairs are replayed, and the enhanced substitution model is further trained; constructing an adversarial sample on the enhanced substitution model by applying a white-box attack strategy, and migrating to a black-box target model to implement attack; the problems that an existing substitution model is low in learning efficiency for different synthetic data, the synthetic data generated by the generator is poor in diversity, and the substitution model is low in training efficiency are solved, and by optimizing the substitution model and the generator learning strategy, the optimized substitution model accurately simulates black box target model decision output.

The invention provides an adversarial sample generation method and system based on a dynamic pseudo tag, and the method comprises the steps: calculating the classification confidence distribution of a sample x based on a white box model, and determining a misjudgment category with the highest confidence as a pseudo tag; calculating a synthetic gradient of a real label and the pseudo label loss function; based on the obtained synthetic gradient, obtaining an adversarial sample xadv; and updating the sample x by using the obtained adversarial sample xadd, repeating the steps, iterating for a preset number of times T, and outputting an adversarial sample xadv. According to the method, the mobility and the generation efficiency of the adversarial sample are improved.

The invention discloses an AI-driven full-scene intelligent test platform which comprises the following steps: performing static scanning on a target code under the triggering of a code submission event through a code static scanning module to generate a scanning result, and then inputting the scanning result into a pre-trained first AI model to obtain code quality evaluation data. And generating a white box test case through a white box case generation module. In addition, a black box test case is generated through a black box case generation module. An automatic script generation module inputs the black box test case into a pre-trained fourth AI model to generate an automatic test script; and finally, running the white box test case and the automatic test script to obtain an original test report. And inputting the original test report into a pre-trained fifth AI model through a result analysis module, and generating defect distribution data and test suggestion data. The full-scene test coverage of the software life cycle is realized, the test of each link of the software is comprehensively and systematically covered, and the test efficiency is improved.

The invention relates to an anti-noise semantic communication method based on supervised discriminant learning, and belongs to the technical field of communication. For the problems of semantic noise and channel noise in image transmission, a deep supervision network is introduced at a semantic task execution end, a supervision layer is additionally arranged behind a hidden layer, and high-quality middle layer features are extracted, so that semantic feature representation is learned; a composite loss function containing terminal loss and adjoint loss is designed, the adjoint loss is introduced into a prototype consistent loss function, and semantic noise influence is reduced through maximum separation constraint; according to the method, the priority of task execution and data recovery is adjusted according to the SNR value so as to adapt to different communication conditions; furthermore, semantic noise is generated by adopting a white box attack method, and the anti-noise performance is enhanced by utilizing a supervision layer output result on the premise of not changing the structure of the original deep learning model; a loss function is designed from the semantic feature level, the anti-noise performance is further enhanced, and data recovery and task execution are both considered.

The invention relates to the field of deep neural network security, in particular to a black box attack method based on gradient alignment and anti-distillation. Comprising the following steps: step 1, designing a sample disturbance generator with a maximized gradient difference; 2, designing a gradient alignment anti-distillation modeling framework for the substitution model; 3, alternately training the substitution model and the disturbance generator; and 4, completing the attack of the black box model based on a white box attack method and the substitution model. Experimental results on different data sets and models show that compared with a traditional static distillation method, a dynamic distillation method based on Gaussian distribution random disturbance and a dynamic distillation method based on adversarial disturbance, the method has the advantages that the adversarial success rate is remarkably improved, and certain innovativeness and universality are achieved.

The invention provides an intention-driven semantic evolution mechanism based on a tensor semantic field and an application system thereof, and relates to the technical field of artificial intelligence, semantic networks and cognitive computing. According to the method, five types of semantic primitives including data, information, knowledge, intelligence and intention are expressed as high-order tensor nodes, a semantic tensor field network is constructed, and dynamic evolution and intention driving of semantics are achieved. The system comprises a multi-scale semantic aggregation mechanism, an intention weight diffusion algorithm, a semantic tensor evolution operator and a white box interpretation interface, supports full-link semantic processing from original data to high-level wisdom to intention constraint, and overcomes the defects in semantic representation and evolution, intention fusion and system interpretability in the prior art. And the generative AI system has stronger intention perception, semantic self-optimization and process transparency capabilities, and is suitable for applications such as a semantic perception large model platform, an AI cognitive map system and an interpretable language generator.

The invention relates to the technical field of chips, and provides an interrupt module verification method and device, a medium and a program product, the method comprises the following steps: when it is monitored that an interrupt module generates an external observable behavior, comparing monitored behavior information with a reference value of the external observable behavior, and verifying the correctness of the external observable behavior; performing state calculation on the interrupt request input to the interrupt module, and determining an internal processing state of the interrupt request; an internal register state of the interrupt module is verified based on an internal processing state of the interrupt request. According to the invention, through a mode of combining black box verification of external observable behaviors with white box verification based on an internal processing state, comprehensive, deep and efficient function verification of a complex interrupt module is realized.

The invention discloses a black box attack optimization method based on gradient prior information, and relates to the technical field of artificial intelligence model security, and the method comprises the steps: in a white box model scene, obtaining a target picture feature and an adversarial sample picture feature based on a target text and a clean picture, generating a migration confrontation sample picture and gradient prior information based on a random fast gradient descent method; calculating a similarity degree estimated value of gradient prior information and a real gradient of a black box model loss function according to a finite difference method, and calculating a synthesis proportion and a disturbance vector; inputting the migration adversarial sample picture into a black box model to obtain a picture description text, performing similarity comparison on a description text feature code and a target text feature code to obtain a loss function, updating disturbance in the migration adversarial sample picture through a stochastic gradient approximation estimation method based on the loss function, and outputting a final adversarial sample. And implementing a black box query attack. According to the method, the attack effect, the attack effect stability and the attack method generality can be improved.

The invention relates to a scene self-adaptive white-box prompt injection attack system. The system comprises a self-adaptive context prompt generation module, an enhanced GCG algorithm-based confrontation suffix generation module and a simulator-based intelligent agent test module with a body. The adaptive context prompt generation module constructs specific prompt templates for different scenes, and automatically identifies and encodes unique semantic features of different application scenes. And the adversarial suffix generation module based on the enhanced GCG algorithm calculates the coordinate gradient of each token position, provides direction guidance for subsequent token replacement based on a double-component loss function, and adopts a weighted combination loss function to realize system optimization. The simulator-based intelligent agent test module carries out semantic understanding and intention recognition through a target large language model, converts a natural language into a control instruction, recognizes different types of commands, and maps the analyzed commands to a specific API for calling.

The invention discloses an attribute-based encryption method and system based on support of multi-authority accountability and outsourcing calculation, and the method specifically comprises the steps: introducing a multi-attribute authority mechanism to be responsible for key distribution and management, reducing the dependence on a single mechanism, and improving the safety and reliability of the system; the cloud server locally updates the access strategy and the ciphertext according to the minimum change increment set uploaded by the data owner; a white-box tracking technology is adopted to bind a user identity with an attribute key, so that a system can locate a specific identity of a malicious user and a specific identity of a malicious attribute authority through a leaked key; intensive computing tasks in the encryption and decryption processes are outsourced to a cloud server, and meanwhile it is ensured that user privacy is not leaked in the outsourcing process; for the untrusted problem of the cloud server, the calculation result is verified by using the smart contract in the block chain, and it is ensured that the verification process is open and transparent and the result is trusted; and an attribute version mark is introduced, so that efficient revocation of old version attributes is realized, and the safety is further improved.

The invention belongs to the technical field of power system stability judgment, and particularly discloses a converter single-feed-in system small interference stability judgment method and system, and the method comprises the steps: constructing admittance matrixes of a device side and a network side of a converter single-feed-in system; obtaining a coupling admittance matrix based on the admittance matrixes of the equipment side and the network side; performing single frequency sweeping on the coupled admittance matrix to obtain a characteristic value of the coupled admittance matrix; obtaining a critical short-circuit ratio of the converter single-feed-in system based on the characteristic value; the short-circuit ratio of the converter single-feed-in system is calculated, if the short-circuit ratio is larger than the critical short-circuit ratio, the small interference of the converter single-feed-in system is stable, and otherwise, the small interference of the converter single-feed-in system is unstable. According to the method, a white box model with all known parameters does not need to be constructed, frequency sweeping does not need to be carried out on a closed-loop system for multiple times, and the problem that a measurement result is inaccurate due to the fact that high-frequency-band data is prone to being polluted is solved; the calculation process is simple, and the stability of the system is improved.