Image data processing method, image data recognition method, training method for image recognition model, image data processing apparatus, training apparatus for image recognition model, and image recognition apparatus

A computer system, comprising:

• one or more processors;
• one or more storage devices coupled to the one or more processors, the one or more storage devices, individually or collective, having executable instructions stored thereon, which when executed by the one or more processors, enable the one or more processors to, individually or collectively, implement acts including:
• performing data anonymization processing on image data based on frequency domain transform to obtain anonymized image data of the image data, the anonymized image data including a first subgraph data set, and each subgraph data in the first subgraph data set corresponding to a different frequency;
• and
• performing image blending processing on the subgraph data in the anonymized image data based on data augmentation, to obtain augmented anonymized image data,
• wherein the performing data anonymization processing on the image data based on frequency domain transform includes:
• performing local frequency domain transform processing on the image data to obtain at least one feature map, wherein each feature map of the at least one feature map includes a plurality of elements and corresponds to a data block in the image data, and each element of the plurality of elements corresponds to a frequency in the frequency domain,
• constructing, by using elements corresponding to frequencies in the at least one feature map, frequency component channel feature maps corresponding to the frequencies, respectively;
• and
• obtaining at least one target frequency component channel feature map from the frequency component channel feature maps through screening, to obtain the anonymized image data of the image data, wherein a target frequency component channel feature map of the at least one target frequency component feature map is a channel feature for image recognition.

A non-transitory storage medium having executable instructions stored thereon, the executable instructions, when executed by one or more processors, enabling the one or more processors to, individually or collectively, implement acts comprising:

• one or more processors;
• one or more storage devices coupled to the one or more processors, the one or more storage devices, individually or collective, having, which when executed by the one or more processors, enable the one or more processors to, individually or collectively, implement acts including:
• performing data anonymization processing on image data based on frequency domain transform to obtain anonymized image data of the image data, the anonymized image data including a first subgraph data set, and each subgraph data in the first subgraph data set corresponding to a different frequency;
• and
• performing image blending processing on the subgraph data in the anonymized image data based on data augmentation, to obtain augmented anonymized image data,
• wherein the data augmentation includes mixup data augmentation, a number of images used for the image blending processing is k, and a maximum weight coefficient for blending image is Wmax;
• and
• the performing image blending processing on the subgraph data in the anonymized image data based on data augmentation includes:
• performing k−1 times of disarrangement processing on the first subgraph data set of the anonymized image data to obtain k subgraph data sets including the first subgraph data set;
• constructing an image hypermatrix with a size of m*k based on the k subgraph data sets, wherein a first column in the image hypermatrix corresponds to the first subgraph data set, and m is a number of pieces of subgraph data in the first subgraph data set;
• randomly generating a weight coefficient for each image in the image hypermatrix;
• performing row normalization on weight coefficients of images in the image hypermatrix, so that a sum of weight coefficients of each row of images is 1, and a weight coefficient of each image is not greater than Wmax;
• and
• performing weighted summation on a row of images of the image hypermatrix to obtain a blended image hypermatrix with a size of m*1, wherein images in the blended image hypermatrix are the augmented anonymized image data.