3results about How to "Good attack performance" patented technology

The application provides a trajectory embedding leakage prevention method and system, including the following: in a test environment, based on similarity calculation, multi-label classification, and recurrent neural network attack trajectory embedding to be attacked, any region in the space region that the original trajectory may pass through is obtained as the predicted privacy information of the original trajectory; a model improvement step: the trajectory embedding model is investigated, evaluated, and improved through the predicted privacy information of the original trajectory; a leakage prevention step: the improved trajectory embedding model is used to prevent trajectory embedding leakage. The application obtains the privacy information of the trajectory in the test environment, achieves good attack effect, overcomes the lack of attack technology in this aspect, and can prevent embedding trajectory leakage.

This invention provides a robust adversarial camouflage generation method, system, and storage medium for monocular depth estimation in multi-view and complex environments. The method includes: Step S1, scene data acquisition: acquiring forward image data during vehicle movement; Step S2, adversarial texture rendering; Step S3, multi-view image acquisition: randomly selecting different angles, distances, and bias parameters, obtaining the corresponding camera positions through a transformation function, and using a differentiable renderer to obtain multiple object images with different angles, distances, and offsets, along with corresponding masks; Step S4, complex physical domain environment enhancement; Step S5, adversarial loss: constructing an adversarial loss function; Step S6, multi-view joint optimization. The beneficial effects of this invention are: overcoming the limitations of existing methods for object detection models and their difficulty in directly transferring to regression tasks, achieving robust multi-view adversarial attacks against texture-sensitive models like MDEs in complex physical domains.

This invention discloses a backdoor attack method for deep neural network models, comprising: constructing and pre-training a spatial adaptive feature selection network; processing images by dividing them into blocks and inputting them into the pre-trained spatial adaptive feature selection network, and introducing a weighted ranking mechanism to generate trigger materials; constructing a feature labeling network, inputting labels into the feature labeling network to generate a label mapping map; and generating poisoned samples using the trigger materials and the label mapping map. This invention's deep neural network model backdoor attack method solves the problem of low attack success rate caused by the simple trigger structure and weak spatial adaptability in existing technologies.