Abstract: This application provides a trusted computing-based local key escrow method, apparatus, device and medium. The method includes: determining an executable file associated with an untrusted environment and a dynamic link file associated with a trusted environment in response to acquiring an enclave interface definition file from a local internal memory; determining an environment access interface based on a container identifier indicated by the trusted environment in response to loading the dynamic link file based on the executable file; reading sealed data file obtained by encrypting serialized data based on a local key in the untrusted environment in response to accessing an enclave container in the trusted environment through the environment access interface; and decrypting the sealed data file using the local key and deserializing the decrypted sealed data file in the enclave container to obtain service data for loading into a trusted internal memory indicated by the enclave container.

Abstract: An image processing method, apparatus, and device, and a storage medium relate to the field of artificial intelligence. The method may include: obtaining an image corresponding to a target battery module, the target battery module including N solder joints, and the N solder joints being respectively mapped to N solder joint fields in the image; dividing the image according to the N solder joint fields to obtain N image regions in one-to-one correspondence with the N solder joint fields; calculating image difference information between each pair of adjacent image regions among the N image regions to obtain an image difference information set; and performing fault recognition on the target battery module based on the image difference information set. The accuracy for recognizing a preset fault in a battery module can be improved by the method.

Abstract: The present disclose relates to a data processing method and device.

Abstract: This disclosure relates to image processing method and apparatus. The method includes: processing, with a first generator in an image processing model, a first sample image in a first sample set to obtain a first predicted image; processing, with the first generator, a second sample image in a second sample set to obtain a second predicted image; and training the image processing model according to a difference between the target avatar in the first sample image and the first predicted avatar and a difference between a first type attribute of the sample avatar in the second sample image and a first type attribute of the second predicted avatar.

Abstract: An image processing method and apparatus that includes: performing feature extraction processing on N image regions of a to-be-processed image respectively to obtain feature data corresponding to the N image regions respectively, N being an integer greater than or equal to 1, performing defect detection on the N image regions respectively according to the feature data corresponding to the N image regions respectively to obtain a prediction probability of a defect in each of the N image regions, obtaining attention for each image region, adjusting the prediction probability of the defect in each image region according to the attention for each image region, and generating a prediction result of the to-be-processed image according to the adjusted prediction probability of each image region.

Abstract: The present disclosure relates to data processing method and apparatus. The method includes: acquiring local model parameters respectively corresponding to N local recognition models; acquiring M parameter fusion modes associated with a local model parameter set, and performing parameter fusion on the local model parameter set respectively according to each parameter fusion mode, so as to obtain M alternative global models; and acquiring evaluation indexes of the M alternative global models respectively in a multimedia verification data set, determining a target global model in the M alternative global models according to the evaluation indexes, and transmitting the target global model to N clients, the N clients updating parameters of a local recognition model associated with the target global model according to the target global model respectively, so as to obtain an object recognition model.

Abstract: An access processing method and apparatus for remotely controlling a terminal, and a storage medium. The method includes: obtaining identity information of a target user from a user terminal, returning a controllable device set corresponding to the target user to the user terminal based on the identity information, receiving, from the user terminal, a target device identifier selected from the controllable device set, and obtaining verification information of a target device indicated by the target device identifier, the verification information including: access information for accessing the target device, and controlling the verification information to be transmitted to the target device to allow, based on verification of the verification information passing, access and remote control on the target device.

Abstract: An image data processing method and apparatus are provided. In a technical solution provided by embodiments of this disclosure, M object feature maps with different sizes are obtained by extracting a source image. While classification confidence levels corresponding to pixel points in each of the object feature maps are acquired, initial predicted polar radii corresponding to the pixel points in each of the object feature maps may also be acquired. The initial predicted polar radii are refined based on polar radius deviations corresponding to the contour sampling points in each of the object feature maps, to acquire target predicted polar radii corresponding to the pixel points in each of the object feature maps. Then the object edge shape of a target object contained in the source image can be determined based on the target predicted polar radii and the classification confidence levels.

Abstract: A honeypot identification method based on cyberspace mapping provides improved accuracy and efficiency of identifying a honeypot. One or more open ports corresponding to a target Internet Protocol address and a target open port for login are determined. Account login information of the target open port is acquired. A service of the target open port is logged into to acquire system environment information. Cyberspace mapping data is determined based on the one or more open ports, port fingerprint information of the one or more open ports, and the system environment information. A honeypot identification result of the target Internet Protocol address is obtained based on the cyberspace mapping data.

Abstract: This disclose relates to a data processing method and apparatus. The method includes: acquiring a first prediction region in a target image, the first prediction region being a prediction region corresponding to a maximum prediction category probability in N prediction regions in the target image, a prediction category probability being a probability that an object in a prediction region belongs to a prediction object category; determining a coverage region jointly covered by a second prediction region and the first prediction region; the second prediction region being a prediction region other than the first prediction region in the N prediction regions; and determining a target prediction region in the prediction regions based on an area of the coverage region and a similarity associated with the second prediction region, the similarity being for indicating a similarity between an object in the second prediction region and an object in the first prediction region.