Customized deep learning classifier for detecting organization sensitive data in images on premises
Disclosed is a method of building a customized deep learning (DL) stack classifier to detect organization sensitive data in images, referred to as image-borne organization sensitive documents, and protecting against loss of the image-borne organization sensitive documents, including distributing a trained feature map extractor stack with stored parameters to an organization, under the organization's control, configured to allow the organization to extract from image-borne organization sensitive documents, feature maps that are used to generate updated DL stacks, without the organization forwarding images of organization-sensitive training examples, and to save non invertible feature maps derived from the images, and ground truth labels for the image. Also included is receiving organization-specific examples including the non-invertible feature maps extracted from the organization-sensitive documents and the ground truth labels and using the received organization-specific examples to generate a customer-specific DL stack classifier. Further included is sending the customer-specific DL stack classifier to the organization.
1. A computer-implemented method of building a customized deep learning (abbreviated DL) stack classifier to detect organization sensitive data in images, referred to as image-borne organization sensitive documents, and protecting against loss of the image-borne organization sensitive documents, including:
distributing a trained feature map extractor stack with stored parameters to an organization, under the organization's control, configured to allow the organization to extract from image-borne organization sensitive documents, feature maps that are used to generate updated DL stacks, without the organization forwarding images of organization-sensitive training examples, and to save non invertible feature maps derived from the images, and ground truth labels for the images;
receiving organization-specific examples including the non-invertible feature maps extracted from the organization-sensitive documents and the ground truth labels; and
using the received organization-specific examples to generate a customer-specific DL stack classifier.
2. The computer-implemented method of claim 1 , further including sending the customer-specific DL stack classifier to the organization.
3. The computer-implemented method of claim 1 , further including delivering the customer-specific DL stack classifier to the organization as an add-on to the feature map extractor stack.
4. The computer-implemented method of claim 1 , wherein the image-borne organization sensitive documents are identification documents.
5. The computer-implemented method of claim 4 , wherein the identification documents in images are one of passport book, driver's license, social security card and payment card.
6. The computer-implemented method of claim 1 , wherein the image-borne organization sensitive documents are screenshot images.
7. The computer-implemented method of claim 1 , further including distorting in perspective the received organization-specific examples to produce a second set of the image-borne organization sensitive documents and using both the received organization-specific examples and the distorted in perspective examples to generate a customer-specific DL stack classifier.
8. A tangible non-transitory computer readable storage media, including program instructions loaded into memory that, when executed on processors, cause the processors to implement a method of building a customized deep learning (abbreviated DL) stack classifier to detect organization sensitive data in images, referred to as image-borne organization sensitive documents, and protecting against loss of the image-borne organization sensitive documents, the method including:
distributing a trained feature map extractor stack with stored parameters to an organization, under the organization's control, configured to allow the organization to extract from image-borne organization sensitive documents, feature maps that are used to generate updated DL stacks, without the organization forwarding images of organization-sensitive training examples, and to save non invertible feature maps derived from the images, and ground truth labels for the images;
receiving organization-specific examples including the non-invertible feature maps extracted from the organization-sensitive documents and the ground truth labels; and
using the received organization-specific examples to generate a customer-specific DL stack classifier.
9. The tangible non-transitory computer readable storage media of claim 8 , further including sending the customer-specific DL stack classifier to the organization.
10. The tangible non-transitory computer readable storage media of claim 8 , further including delivering the customer-specific DL stack classifier to the organization as an add-on to the feature map extractor stack.
11. The tangible non-transitory computer readable storage media of claim 8 , wherein the image-borne organization sensitive documents are identification documents in images.
12. The tangible non-transitory computer readable storage media of claim 11 , wherein the identification documents in images are one of passport book, driver's license, social security card and payment card.
13. The tangible non-transitory computer readable storage media of claim 8 , wherein the image-borne organization sensitive documents are screenshot images.
14. The tangible non-transitory computer readable storage media of claim 8 , further including distorting in perspective the received organization-specific examples to produce a second set of the image-borne organization sensitive documents and using both the received organization-specific examples and the distorted in perspective examples to generate a customer-specific DL stack classifier.
15. A system for building a customized deep learning (abbreviated DL) stack classifier to detect organization sensitive data in images, referred to as image-borne organization sensitive documents, and protecting against loss of the image-borne organization sensitive documents, the system including a processor, memory coupled to the processor, and computer instructions that, when executed on the processors, implement actions comprising:
distributing a trained feature map extractor stack with stored parameters to an organization, under the organization's control, configured to allow the organization to extract from image-borne organization sensitive documents, feature maps that are used to generate updated DL stacks, without the organization forwarding images of organization-sensitive training examples, and to save non invertible feature maps derived from the images, and ground truth labels for the images;
receiving organization-specific examples including the non-invertible feature maps extracted from the organization-sensitive documents and the ground truth labels; and
using the received organization-specific examples to generate a customer-specific DL stack classifier.
16. The system of claim 15 , further including sending the customer-specific DL stack classifier to the organization.
17. The system of claim 15 , further including delivering the customer-specific DL stack classifier to the organization as an add-on to the feature map extractor stack.
18. The system of claim 15 , wherein the image-borne organization sensitive documents are identification documents in images.
19. The system of claim 15 , wherein the image-borne organization sensitive documents are screenshot images.
20. The system of claim 15 , further including distorting in perspective the received organization-specific examples to produce a second set of the image-borne organization sensitive documents and using both the received organization-specific examples and the distorted in perspective examples to generate a customer-specific DL stack classifier.