Image classification and accelerated classification training using deep learning image fingerprinting models and indexed embeddings

A network security system, comprising:

• one or more processors;
• and
• one or more computer-readable memory devices, comprising:
• an image fingerprinting model trained to generate an image fingerprint embedding comprising a multi-dimensional feature vector representing visual aspects of an input image,
• a first plurality of image fingerprint embeddings generated by the image fingerprinting model from a first plurality of images, and
• a classifier training component comprising instructions that, upon execution by the one or more processors, cause the one or more processors to:
• receive a user-defined label and a second plurality of images uploaded by a user, wherein the images in the second plurality of images are distinct from the images in the first plurality of images;
• generate a second plurality of image fingerprint embeddings, the instructions to generate comprising instructions that cause the one or more processors to:
• provide each of the second plurality of images as input to the image fingerprint model, and
• receive each of the second plurality of image fingerprint embeddings as output from the image fingerprinting model;
• train a user-defined image classifier based on and in response to receiving the user-defined label and the second plurality of images, the user-defined image classifier comprising an index and an approximate nearest neighbors model, wherein the instructions to train comprise instructions to:
• generate the index comprising the first plurality of image fingerprint embeddings and the second plurality of image fingerprint embeddings, wherein the instructions to generate the index comprises instructions to:
• generate at least one embedding cluster comprising at least a portion of the first plurality of image fingerprint embeddings;
• generate a user-defined embedding cluster comprising the second plurality of image fingerprint embeddings;
• apply a label to each of the at least one embedding cluster;
• and
• apply the user-defined label to the user-defined embedding cluster, and
• generate the approximate nearest neighbors model associated with the index, wherein:
• the approximate nearest neighbors model is trained to classify subject images using the index;
• and
• the instructions to train does not include deep learning.

A computer-readable storage device having stored thereon instructions that, upon execution by one or more processors, cause the one or more processors to:

• generate a first plurality of image fingerprint embeddings, the instructions to generate comprising instructions that cause the one or more processors to:
• provide each of a first plurality of images as input to an image fingerprinting model, wherein the image fingerprinting model is trained to generate an image fingerprint embedding comprising a multi-dimensional feature vector representing visual aspects of the input image, and
• receive each of the first plurality of image fingerprint embeddings as output from the image fingerprinting model;
• receive a user-defined label and a second plurality of images uploaded by a user, wherein the images in the second plurality of images are distinct from the images in the first plurality of images;
• generate a second plurality of image fingerprint embeddings, the instructions to generate comprising instructions that cause the one or more processors to:
• provide each of the second plurality of images as input to the image fingerprinting model, and
• receive each of the second plurality of image fingerprint embeddings as output from the image fingerprinting model;
• train a user-defined image classifier based on and in response to receiving the user-defined label and the second plurality of images, the user-defined image classifier comprising an index and an approximate nearest neighbors model, the instructions to train comprising instructions to:
• generate the index comprising the first plurality of image fingerprint embeddings and the second plurality of image fingerprint embeddings, wherein the instructions to generate the index comprises instructions to:
• generate at least one embedding cluster comprising at least a portion of the first plurality of image fingerprint embeddings;
• generate a user-defined embedding cluster comprising the second plurality of image fingerprint embeddings;
• apply a label to each of the at least one embedding cluster;
• and
• apply the user-defined label to the user-defined embedding cluster, and
• generate the approximate nearest neighbors model associated with the index, wherein:
• the approximate nearest neighbors model is trained to classify subject images using the index;
• and
• the instructions to train the user-defined image classifier do not include deep learning.