11results about How to "Increase aggressiveness" patented technology

The utility model belongs to the technical field of drilling tools, and particularly relates to a self-driven rotating tooth and a drill bit. The self-driven rotating tooth comprises a rotating sleeve; the core tooth is coaxially and rotatably arranged on the rotating sleeve, a driving combination surface is arranged on the working surface of the core tooth, and the driving combination surface is configured to enable the core tooth to rotate relative to the rotating sleeve when being subjected to axial force. The working face of the core tooth is provided with the driving combination face, so that the core tooth is constructed into the special-shaped tooth, on one hand, the core tooth can automatically rotate in the drilling process without a side corner, can be installed at the position of a cutting tooth of a conventional PDC drill bit for use, and can be exchanged with the conventional PDC cutting tooth for use; and large-area popularization and application of the rotating tooth are facilitated, and the impact resistance and aggressiveness of the core tooth can be improved. On the other hand, the impact resistance and aggressiveness of the core tooth can be improved.

The invention provides a bispecific antibody capable of binding to PD-L1 and VEGF (vascular endothelial growth factor). The bispecific antibody comprises a first binding domain capable of binding to PD-L1 and a second binding domain capable of binding to VEGF. The bispecific antibody provided by the invention is combined with PD-L1, so that the immunosuppression of tumor cells on T cells can be relieved, and the anti-tumor function of an immune system can be recovered; meanwhile, by combining the bispecific antibody with VEGF, the growth of the tumor can be slowed down, the blood supply of the tumor can be reduced, and even the tumor blood vessel can be normalized, so that the immune environment is improved, and the attack ability of immune cells on the tumor is enhanced.

This invention provides an artificial intelligence (AI) model security detection system based on adversarial example injection, relating to the field of AI security. The system includes the following modules: an adversarial example generation module for dynamically generating adversarial examples for the AI ​​model; a multi-level injection module for controlling the injection of adversarial examples at each level of the AI ​​model; an adversarial example fusion module for intelligently coordinating the generation and fusion of adversarial examples with physical constraints and those with digital perturbations; a multi-dimensional monitoring module for real-time monitoring of the AI ​​model's output, internal features, and resource consumption; a security assessment module based on a deep metric learning algorithm to output assessment results; and a defense suggestion module to generate defense schemes based on the assessment results. This invention improves the efficiency of AI model security detection and provides reliable technical support for the secure deployment and continuous optimization of AI models.

The application relates to a conical complex piece and a drill bit, which comprises a base and a conical tooth arranged on the top surface of the base, the bottom end of the conical tooth is connected with the top surface of the base, the top end of the conical tooth is provided with an impact surface for contacting with the external environment, first recessed surfaces and second recessed surfaces are arranged on the circumferential outer wall of the conical tooth and are spaced apart in the circumferential direction, and the side edges of the first recessed surfaces and the second recessed surfaces which are close to each other both extend along the generatrix direction of the conical tooth, and a ridge is formed between the first recessed surfaces and the second recessed surfaces. The ridge can effectively scrape the stratum structure during rotation, improves the attackability and the ability of eating into the stratum, and finally improves the efficiency of the conical complex piece during drilling. In addition, the impact surface arranged at the top end of the conical complex piece has strong impact resistance when contacting with the stratum, and finally the drill bit has strong impact resistance and strong scraping effect.

This invention relates to fields such as AI adversarial techniques, and discloses a data-free black-box adversarial example generation method based on attention enhancement, comprising: randomly sampling a noise space, inputting it into a generator to generate synthetic data. ; Utilizing synthetic data Query black-box target model The system calculates the decision output and caches the input-output pairs; it strengthens the alternative model using an attention enhancement module and trains the enhanced alternative model; it trains the generator using a joint structural information learning strategy; it replays the cached input-output pairs to further train the enhanced alternative model; it employs a white-box attack strategy to construct adversarial examples on the enhanced alternative model and transfers them to the black-box target model to carry out the attack; it overcomes the problems of low learning efficiency of existing alternative models for different synthetic data, poor diversity of synthetic data generated by the generator, and low training efficiency of alternative models, and by optimizing the learning strategies of the alternative model and the generator, the optimized alternative model can accurately simulate the decision output of the black-box target model.

This invention discloses a multi-objective evolutionary modeling method for fake data attacks in power system CPS (Computer-Powered Systems), belonging to the field of data attack technology. It solves the problem of insufficient attack flexibility in existing technologies, which fails to provide a foundation for power system defense. This invention models attacks through a three-step attack-defense game: attack gene generation, optimization, and evolution. In the gene generation stage, gene encoding technology is used to construct attack gene units from the decoupled attack offset and tag policy matrix, and these units are then assembled into base pair structures by aligning and splicing them. In the gene optimization stage, a multi-objective fitness function is constructed based on concealment and attack effectiveness objectives. Using residuals and attack effectiveness as objectives, a multi-objective collaborative screening between destructiveness and concealment is achieved to obtain optimized genes. In the gene evolution stage, multi-point crossover and single-point crossover operations are used for the attack chain and concealment chain of the optimized genes, respectively, to enhance the attack effectiveness.

This application discloses an image processing method, apparatus, device, and storage medium, belonging to the field of artificial intelligence technology. The method includes: acquiring an original image; performing feature encoding processing on the original image to obtain a first feature map; acquiring a second feature map and a third feature map of the original image based on the first feature map; wherein the second feature map represents an image perturbation to be superimposed on the original image, and each position on the third feature map has different feature values, each feature value being used to characterize the importance of the image feature at the corresponding position; generating a noisy image based on the second and third feature maps; and superimposing the original image and the noisy image to obtain a first adversarial example. This application can generate high-quality adversarial examples, thereby achieving good attack effects.

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 an active face image protection method, system, and storage medium, relating to the field of network information security technology. The method includes: acquiring the original face image to be protected; generating an adversarial perturbation intended to be superimposed onto the original face image; iteratively optimizing the adversarial perturbation based on preset optimization objectives and constraints; wherein the optimization objective is configured to calculate and maximize the geometric position difference of facial key points between the original face image and the image after superimposing the adversarial perturbation, thereby disrupting the facial geometric structure features in the protected image; the constraints include joint spatial and frequency domain constraints, using a frequency domain mask to apply differentiated constraints to the coefficient values ​​of the adversarial perturbation in the frequency domain, and limiting the maximum amplitude of each pixel in the spatial domain; finally, superimposing the adversarial perturbation that meets the conditions onto the original face image to obtain the face protection image. This invention can effectively defend against malicious semantic editing of generative models while maintaining the visual quality of the image.

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.

This invention belongs to the field of image processing and discloses a method and related apparatus for generating adversarial images for deepfake detection models. The method includes acquiring an original image and inputting it into a preset adversarial artificial degradation model to obtain an initial adversarial image; inputting the initial adversarial image into a preset image restoration model to obtain an adversarial image; wherein the adversarial artificial degradation model is obtained by: acquiring an image dataset containing the initial image and a degradation image based on the initial image; constructing the initial artificial degradation model and training it using the image dataset to obtain an artificial degradation model; and training the adversarial nature of the artificial degradation model using the image dataset based on a joint loss to obtain the adversarial artificial degradation model. This method helps to transform the distribution of adversarial images into the distribution of real images, avoids significant noise patterns, possesses high visual quality and fidelity, provides guidance for optimizing deepfake detection models, and thus improves the quality of deepfake image detection.