3results about How to "Improve attack efficiency" patented technology

The application relates to the field of power systems and artificial intelligence security, and discloses a simulation attack and defense method of a power grid load frequency control system adopting a DRL controller, which comprises the following steps of: simulation attack: identifying system state variables most critical to DRL decision; setting an attack target and calculating an initial disturbance amount of the critical state variables, limiting the generation of a final disturbance amount; deducing disturbance amounts of the remaining dependent state variables to obtain an adversarial disturbance vector, which is superimposed to the DRL controller to implement the attack. Simulation defense: applied to a hybrid system containing DRL and a backup PID controller: real-time monitoring of the Q value sequence output by the DRL evaluation network to detect whether it is continuously lower than the normal benchmark; once the statistical quantity exceeds the threshold, it is determined that an attack is suffered, and the control right is immediately switched from the DRL to the PID controller to maintain the stability of the system. The method can generate a hidden and physically regular adversarial attack to evaluate the vulnerability of the system, and provide a fast and low-cost active defense mechanism to improve the security and resilience of the LFC system in the adversarial environment.

This invention provides an scalable AI attack benchmark testing method and system. A model conversion module transforms the tested model according to its type and framework, obtaining a converted model that matches the attack framework. The dataset attacks the converted model, yielding a test result. The attack framework invokes various attack algorithms with a standardized format, generates corresponding attack scripts, and then performs several attacks on the converted model, resulting in multiple attack results. When testing the next tested model, the model conversion module again transforms its framework according to its type before launching the attack. The advantages are: it can flexibly convert for different AI models, attacking the converted model to obtain attack results, without needing to rebuild test modules for different AI models, improving attack efficiency, reducing testing costs, and solving the problem of existing testing methods (frameworks) being limited by the tested model and language.

This invention discloses an adversarial example generation method, system, storage medium, and terminal, belonging to the field of adversarial attack technology. The method includes: determining the context region of a target object and dividing it into multiple sub-regions; performing importance analysis on each sub-region; mapping the influence of each sub-region on the detection results of the target detection model to the context region, generating a context attribution graph; selecting one or more influential sub-regions exceeding an influence threshold; and fine-tuning the influential sub-regions under a total loss function (including a loss function and a perceptual loss function) to generate adversarial examples. This invention accurately guides the attack direction through the context attribution graph and introduces a dual loss function, suppressing position, direction, and confidence prediction while ensuring the visual imperceptibility of adversarial perturbations, thus preserving the integrity of the target object and enhancing the stealth of the attack. Attacking only some influential sub-regions improves attack efficiency.