IP Library › Granted Patent US 12,243,294
Granted Patent B1
US 12,243,294 · App. 18/451,014 · Granted Mar 4, 2025

Image fingerprinting conversion between different image fingerprinting models

Inventors: Jason B. Bryslawskyj (San Diego, CA); Yi Zhang (Santa Clara, CA); Ari Azarafrooz (Rancho Santa Margarita, CA); Wayne Xin (Santa Clara, CA); Yihua Liao (Palo Alto, CA); Niranjan Koduri (Pleasanton, CA); Emanoel Daryoush (San Jose, CA)
Assignee: Netskope, Inc.
G06V10/774G06V10/761G06V10/762G06V10/764
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Quick Facts
Patent No.
US 12,243,294
App. No.
18/451,014
Granted
Mar 4, 2025
Kind
B1
Abstract

Image fingerprints (embeddings) are generated by an image fingerprinting model and indexed with an approximate nearest neighbors (ANN) model trained to identify the most similar fingerprint based on a subject embedding. For image matching, a score is provided that indicates a similarity between the input embedding and the most similar identified embedding, which allows for matching even when an image has been distorted, rotated, cropped, or otherwise modified. For image classification, the embeddings in the index are clustered and the clusters are labeled. Users can provide just a few images to add to the index as a labeled cluster. The ANN model returns a score and label of the most similar identified embedding for labeling the subject image if the score exceeds a threshold. As improvements are made to the image fingerprinting model, a converter model is trained to convert the original embeddings to be compatible with the new embeddings.

Claims (98)

1. A computer-implemented method, comprising:

generating a plurality of first image fingerprint embeddings, the generating comprising:

providing each of a plurality of images as input to a first image fingerprinting model, wherein the first image fingerprinting model is trained to generate a first image fingerprint embedding comprising a multi-dimensional feature vector representing visual aspects of the input image, and

receiving each of the plurality of first image fingerprint embeddings as output from the first image fingerprinting model;

generating a plurality of second image fingerprint embeddings, the generating comprising:

providing each of the plurality of images as input to a second image fingerprinting model, wherein the second image fingerprinting model is trained to generate a second image fingerprint embedding comprising a multi-dimensional feature vector representing visual aspects of the input image, and

receiving each of the plurality of second image fingerprint embeddings as output from the second image fingerprinting model, wherein the first image fingerprint embedding is different than the second image fingerprint embedding generated for each of the plurality of images;

training a converter model to convert a first image fingerprint embedding to a second image fingerprint embedding compatible with other second image fingerprint embeddings, the training comprising:

for each image of the plurality of images:

providing the first image fingerprint embedding associated with the respective image as input to the converter model,

receiving an output image fingerprint embedding from the converter model,

comparing the output image fingerprint embedding with the second image fingerprint embedding associated with the respective image to calculate a loss, and

backpropagating the loss through the converter model;

accessing a plurality of stored first image fingerprint embeddings;

providing each of the stored first image fingerprint embeddings as input to the converter model to generate a second plurality of second image fingerprint embeddings; and

adding the second plurality of second image fingerprint embeddings to an index comprising a third plurality of second image fingerprint embeddings generated by the second image fingerprinting model, wherein the index is used by an approximate nearest neighbors model.

2. The computer-implemented method of claim 1 , wherein the converter model is an autoencoder.

3. The computer-implemented method of claim 1 , wherein the comparing uses Mean Square Error to calculate the loss.

4. The computer-implemented method of claim 1 , wherein the first image fingerprinting model is a first version of an image fingerprinting model, and the second image fingerprinting model is a second version of the image fingerprinting model.

5. The computer-implemented method of claim 1 , wherein a conversion second image fingerprint embedding generated by the converter model and an original second image fingerprint embedding generated by the second image fingerprinting model are not identical but are exchangeable.

6. The computer-implemented method of claim 1 , wherein the plurality of images comprises images that were not used to train the first image fingerprinting model or the second image fingerprinting model.

7. The computer-implemented method of claim 1 , further comprising analyzing a subject image, the analyzing comprising:

providing the subject image as input to the second image fingerprinting model;

receiving a subject image fingerprint embedding as output from the second image fingerprinting model;

providing the subject image fingerprint embedding as input to the approximate nearest neighbors model trained to retrieve a most similar image fingerprint embedding from the index and generate a score indicating a similarity of the subject image fingerprint embedding to the most similar image fingerprint embedding;

analyzing the score received from the approximate nearest neighbors model; and

performing a security action based on the score.

8. The computer-implemented method of claim 1 , further comprising:

clustering subsets of the second image fingerprint embeddings in the index into embedding clusters; and

applying a label to each of the embedding clusters.

9. The computer-implemented method of claim 8 , further comprising analyzing a subject image, the analyzing comprising:

providing the subject image as input to the second image fingerprinting model;

receiving a subject image fingerprint embedding as output from the second image fingerprinting model;

providing the subject image fingerprint embedding as input to the approximate nearest neighbors model trained to retrieve a label of a most similar second image fingerprint embedding from the index and generate a score indicating a similarity of the subject image fingerprint embedding to the most similar second image fingerprint embedding;

analyzing the score received from the approximate nearest neighbors model; and

labeling the subject image based on the score.

10. A system, comprising:

one or more processors; and

one or more computer-readable memory devices, comprising:

a database storing a plurality of images,

a first image fingerprinting model trained to generate a first image fingerprint embedding comprising a multi-dimensional feature vector representing visual aspects of an input image,

a second image fingerprinting model trained to generate a second image fingerprint embedding comprising a multi-dimensional feature vector representing visual aspects of an input image,

an untrained converter model,

a converter training component comprising instructions that, upon execution by the one or more processors, cause the one or more processors to:

generate a plurality of first image fingerprint embeddings, the instructions to generate comprising instructions that cause the one or more processors to:

providing each of a plurality of images as input to the first image fingerprinting model, and

receive each of the plurality of first image fingerprint embeddings as output from the first image fingerprinting model;

generate a plurality of second image fingerprint embeddings, the instructions to generate comprising instructions that cause the one or more processors to:

provide each of the plurality of images as input to the second image fingerprinting model, and

receive each of the plurality of second image fingerprint embeddings as output from the second image fingerprinting model, wherein the first image fingerprint embedding is different than the second image fingerprint embedding generated for each of the plurality of images; and

for each image of the plurality of images:

provide the first image fingerprint embedding associated with the respective image as input to the untrained converter model,

receive an output image fingerprint embedding from the untrained converter model,

compare the output image fingerprint embedding with the second image fingerprint embedding associated with the respective image to calculate a loss, and

backpropagate the loss through the untrained converter model to generate a trained converter model trained to convert a first image fingerprint embedding generated by the first image fingerprinting model to a second image fingerprint embedding compatible with other second image fingerprint embeddings generated by the second image fingerprinting model, and

a conversion component comprising instructions that, upon execution by the one or more processors, cause the one or more processors to:

access a plurality of stored first image fingerprint embeddings;

provide each of the stored first image fingerprint embeddings as input to the trained converter model to generate a second plurality of second image fingerprint embeddings; and

add the second plurality of second image fingerprint embeddings to an index comprising a third plurality of second image fingerprint embeddings generated by the second image fingerprinting model, wherein the index is used by an approximate nearest neighbors model.

11. The system of claim 10 , wherein the trained converter model is an autoencoder.

12. The system of claim 10 , wherein the instructions to compare comprises instructions to calculate the loss using Mean Square Error.

13. The system of claim 10 , wherein the first image fingerprinting model is a first version of an image fingerprinting model, and the second image fingerprinting model is a second version of the image fingerprinting model.

14. The system of claim 10 , wherein a conversion second image fingerprint embedding generated by the trained converter model and an original second image fingerprint embedding generated by the second image fingerprinting model are not identical but are exchangeable.

15. The system of claim 10 , wherein the plurality of images comprises images that were not used to train the first image fingerprinting model or the second image fingerprinting model.

16. The system of claim 10 , the one or more computer-readable memory devices further comprising:

a subject image analysis component comprising instructions that, upon execution by the one or more processors, cause the one or more processors to:

provide a subject image as input to the second image fingerprinting model;

receive a subject image fingerprint embedding as output from the second image fingerprinting model;

provide the subject image fingerprint embedding as input to the approximate nearest neighbors model trained to retrieve a most similar image fingerprint embedding from the index and generate a score indicating a similarity of the subject image fingerprint embedding to the most similar image fingerprint embedding;

analyzing the score received from the approximate nearest neighbors model; and

performing a security action based on the score.

17. The system of claim 10 , the conversion component comprising further instructions that, upon execution by the one or more processors, cause the one or more processors to:

cluster subsets of the second image fingerprint embeddings in the index into embedding clusters; and

apply a label to each of the embedding clusters.

18. The system of claim 17 , the one or more computer-readable memory devices further comprising:

an image classification component comprising instructions that, upon execution by the one or more processors, cause the one or more processors to:

provide a subject image as input to the second image fingerprinting model;

receive a subject image fingerprint embedding as output from the second image fingerprinting model;

provide the subject image fingerprint embedding as input to the approximate nearest neighbors model trained to retrieve a label of a most similar second image fingerprint embedding from the index and generate a score indicating a similarity of the subject image fingerprint embedding to the most similar second image fingerprint embedding;

analyze the score received from the approximate nearest neighbors model; and

label the subject image based on the score.

19. 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 plurality of first image fingerprint embeddings, wherein the instructions to generate comprise instructions that, upon execution by the one or more processors, cause the one or more processors to:

provide each of a plurality of images as input to a first image fingerprinting model, wherein the first image fingerprinting model is trained to generate a first image fingerprint embedding comprising a multi-dimensional feature vector representing visual aspects of the input image, and

receive each of the plurality of first image fingerprint embeddings as output from the first image fingerprinting model;

generate a plurality of second image fingerprint embeddings, wherein the instructions to generate comprise instructions that, upon execution by the one or more processors, cause the one or more processors to:

provide each of the plurality of images as input to a second image fingerprinting model, wherein the second image fingerprinting model is trained to generate a second image fingerprint embedding comprising a multi-dimensional feature vector representing visual aspects of the input image, and

receive each of the plurality of second image fingerprint embeddings as output from the second image fingerprinting model, wherein the first image fingerprint embedding is different than the second image fingerprint embedding generated for each of the plurality of images;

train a converter model to convert a first image fingerprint embedding to a second image fingerprint embedding compatible with other second image fingerprint embeddings, wherein the instructions to train comprise instructions that, upon execution by the one or more processors, cause the one or more processors to:

for each image of the plurality of images:

provide the first image fingerprint embedding associated with the respective image as input to the converter model,

receive an output image fingerprint embedding from the converter model,

compare the output image fingerprint embedding with the second image fingerprint embedding associated with the respective image to calculate a loss, and

backpropagate the loss through the converter model;

access a plurality of stored first image fingerprint embeddings;

provide each of the stored first image fingerprint embeddings as input to the converter model to generate a second plurality of second image fingerprint embeddings; and

add the second plurality of second image fingerprint embeddings to an index comprising a third plurality of second image fingerprint embeddings generated by the second image fingerprinting model, wherein the index is used by an approximate nearest neighbors model.

20. The computer-readable storage device of claim 19 , wherein the converter model is an autoencoder.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 24, 2023
From: BRYSLAWSKYJ, JASON B.; ZHANG, YI; AZARAFROOZ, ARI; XIN, WAYNE; LIAO, YIHUA; KODURI, NIRANJAN; DARYOUSH, EMANOEL
To: NETSKOPE, INC.
Reel/Frame 064694/0189 →
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