Deep learning-based detection and data loss prevention of image-borne sensitive documents

1. A computer-implemented method of customizing a deep learning (abbreviated DL) stack 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 master DL stack with stored parameters to a plurality of organizations;

providing at least some of the organizations with a DL stack update trainer, under the organizations' control, configured to allow the organizations to perform update training to generate updated DL stacks, without the organizations forwarding images of organization-sensitive training examples, and to save non invertible features derived from the images, ground truth labels for the images, and parameters of the updated DL stacks;

receiving, from a plurality of the DL stack update trainers, organization-specific examples including the non-invertible features of the organization-sensitive training examples and the ground truth labels; and

using the received organization-specific examples to update the trained master DL stack.

2. The computer-implemented method of claim 1 , further including:

directing the organization-sensitive training examples to training of a second set of layers of the trained master DL stack, overlaying a general image processing first set of layers;

storing updated parameters of the second set of layers for inference from production images; and

distributing the updated parameters of the second set of layers to the plurality of organizations.

3. The computer-implemented method of claim 1 , further including performing from-scratch re-training, with the organization-sensitive training examples and non-invertible features from other examples used to train the trained master DL stack, to re-train the trained master DL stack.

4. 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 computer-implemented method of customizing a deep learning (abbreviated DL) stack 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 computer-implemented method including:

distributing a trained master DL stack with stored parameters to a plurality of organizations;

providing at least some of the organizations with a DL stack update trainer, under the organizations' control, configured to allow the organizations to perform update training to generated updated DL stacks, without the organizations forwarding images of organization-sensitive training examples, and to save non invertible features derived from the images, ground truth labels for the images, and parameters of the updated DL stacks;

receiving, from a plurality of the DL stack update trainers, organization-specific examples including the non-invertible features of the organization-sensitive training examples and the ground truth labels; and

using the received organization-specific examples to update the trained master DL stack.

5. The tangible non-transitory computer readable storage media of claim 4 , further implementing:

directing the organization-sensitive training examples to training of a second set of layers of the trained master DL stack, overlaying a general image processing first set of layers;

storing updated parameters of the second set of layers for inference from production images; and

distributing the updated parameters of the second set of layers to the plurality of organizations.

6. The tangible non-transitory computer readable storage media of claim 4 , further implementing from-scratch re-training, with the organization-sensitive training examples and non-invertible features from other examples used to train the trained master DL stack, to re-train the trained master DL stack.

7. A system for customizing a deep learning (abbreviated DL) stack including a processor, memory coupled to the processor, and computer instructions from the non-transitory computer readable storage media of claim 4 loaded into the memory.

8. A system for customizing a deep learning (abbreviated DL) stack including a processor, memory coupled to the processor, and computer instructions from the non-transitory computer readable storage media of claim 5 loaded into the memory.

9. A system for customizing a deep learning (abbreviated DL) stack including a processor, memory coupled to the processor, and computer instructions from the non-transitory computer readable storage media of claim 6 loaded into the memory.

10. A computer-implemented method of customizing a deep learning (abbreviated DL) stack 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 master DL stack with stored parameters to a plurality of organizations;

providing at least some of the organizations with a DL stack update trainer, under the organizations' control, configured to allow the organizations to perform update training to generate updated DL stacks, without the organizations forwarding images of organization-sensitive training examples, and to save non invertible features derived from the images, labels for the images, and parameters of the updated DL stacks;

receiving, from a plurality of the DL stack update trainers, organization-specific parameters of updated DL stacks; and

using the received organization-specific parameters of updated DL stacks to update the trained master DL stack.

11. The computer-implemented method of claim 10 , further including:

the DL stack update trainers configured to forward updated coefficients from a second set of layers, overlaying a general image processing first set of layers;

receiving from a plurality of the DL stack update trainers, respective updated coefficients from respective second sets of layers;

combining the updated coefficients from respective second sets of layers to further train the second set of layers of the trained master DL stack;

storing updated parameters of the second set of layers of the trained master DL stack for inference from production images; and

distributing the updated parameters of the second set of layers to the plurality of organizations.

12. The computer-implemented method of claim 10 , further including:

storing updated parameters of a second set of layers for inference from production images; and

distributing the updated parameters of the second set of layers to the plurality of organizations.

13. 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 computer-implemented method of customizing a deep learning (abbreviated DL) stack 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 computer-implemented method including:

distributing a trained master DL stack with stored parameters to a plurality of organizations;

providing at least some of the organizations with a DL stack update trainer, under the organizations' control, configured to allow the organizations to perform update training to generate updated DL stacks, without the organizations forwarding images of organization-sensitive training examples, and to save non invertible features derived from the images, labels for the images, and parameters of the updated DL stacks;

receiving, from a plurality of the DL stack update trainers, organization-specific parameters of updated DL stacks; and

using the received organization-specific parameters of updated DL stacks to update the trained master DL stack.

14. The tangible non-transitory computer readable storage media of claim 13 , further including:

the DL stack update trainers configured to forward updated coefficients from a second set of layers, overlaying a general image processing first set of layers;

receiving from a plurality of the DL stack update trainers, respective updated coefficients from respective second sets of layers;

combining the updated coefficients from respective second sets of layers to further train the second set of layers of the trained master DL stack;

storing updated parameters of the second set of layers of the trained master DL stack for inference from production images; and

distributing the updated parameters of the second set of layers to the plurality of organizations.

15. The tangible non-transitory computer readable storage media of claim 13 , further implementing:

storing updated parameters of a second set of layers for inference from production images; and

distributing the updated parameters of the second set of layers to the plurality of organizations.

16. A system for customizing a deep learning (abbreviated DL) stack including a processor, memory coupled to the processor, and computer instructions from the non-transitory computer readable storage media of claim 13 loaded into the memory.

17. A system for customizing a deep learning (abbreviated DL) stack including a processor, memory coupled to the processor, and computer instructions from the non-transitory computer readable storage media of claim 14 loaded into the memory.

18. A system for customizing a deep learning (abbreviated DL) stack including a processor, memory coupled to the processor, and computer instructions from the non-transitory computer readable storage media of claim 15 loaded into the memory.