Abstract: A model determining method including obtaining a first image sample, inputting the first image sample to an initial encoder of an initial identification model to obtain a first image sample feature, generating a second image sample feature based on the first image sample feature, separately inputting the first and second image sample features to an initial decoder to obtain a first texture image and a second texture image, inputting the first image sample feature and the first texture image to an initial classifier to obtain a first prediction result, inputting the second image sample feature and the second texture image to the initial classifier to obtain a second prediction result, generating an identification loss function based on a difference between each of the first and the second prediction results and an identification tag, and training an initial identification model using the identification loss function to obtain an updated identification model.

Abstract: A data processing method includes obtaining N sample image sets and an image recognition model, obtaining a sampled sample image from the N sample image sets, inputting the sampled sample image and the wrong class label into the image recognition model to generate a first probability vector of the sampled sample image for the N class labels, obtaining, from the N probability elements in the first probability vector, a first probability element indicating the wrong class label, and adjusting the sampled sample image based on the first probability element, to obtain an adversarial sample image corresponding to the sampled sample image.

Abstract: A method and apparatus for extracting biological features, a device, a medium, and a program product, relating to the field of image processing. The method includes generating an image sequence according to a biological feature image, the image sequence including the biological feature image and n local image blocks, the local image blocks being obtained by segmenting the biological feature image, n being a positive integer, generating a feature vector group based on the image sequence, feature vectors in the feature vector group being generated based on a feature information interaction result between the biological feature image and the local image blocks and a feature information interaction result between the local image blocks, and taking a feature vector corresponding to the biological feature image in the feature vector group as biological feature information of the biological feature image.

Abstract: A liveness detection method is provided. In the method, image features are extracted from an image of a user. Convolution processing is performed on the image features through an estimation network to obtain a predicted mean value and a predicted variance of the image features. Standardization processing is performed on the image features based on the predicted mean value, the predicted variance, and predetermined network parameters of the standardization processing to obtain standardized features. Whether the image of the user includes a living body image is determined according to a liveness classification probability of a classification performed on the image of the user based on the standardized features. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

Abstract: This invention is involved in the field of pharmaceutical technology. It is related to a triazine compound for resisting coccidiosis in chickens, its preparation method and application. This compound has the structure below.