Systems and methods for private authentication with helper networks
Helper neural network can play a role in augmenting authentication services that are based on neural network architectures. For example, helper networks are configured to operate as a gateway on identification information used to identify users, enroll users, and/or construct authentication models (e.g., embedding and/or prediction networks). Assuming, that both good and bad identification information samples are taken as part of identification information capture, the helper networks operate to filter out bad identification information prior to training, which prevents, for example, identification information that is valid but poorly captured from impacting identification, training, and/or prediction using various neural networks. Additionally, helper networks can also identify and prevent presentation attacks or submission of spoofed identification information as part of processing and/or validation.
1. An authentication system for privacy-enabled authentication, the system comprising:
at least one processor operatively connected to a memory;
an authentication data gateway, executed by the at least one processor, configured to filter identification information used in enrollment, identification, or authentication functions of subsequent neural networks, the authentication data gateway comprising at least a plurality of pre-trained validation helper networks, including:
a first pre-trained validation helper network associated with identification information of a first type, wherein the first pre-trained validation helper network is configured to:
evaluate an unknown identification sample of the first type, responsive to input of the unknown identification sample of the first type to the first pre-trained validation helper network, wherein evaluation is based on evaluation criteria that is independent of a subject of the identification information seeking to be enrolled, identified, or authenticated;
wherein the authentication data gateway is further configured to:
validate the unknown information sample for use in subsequent enrollment, identification, or authentication, responsive to a determination that the unknown identification sample meets the evaluation criteria;
reject the unknown identification sample for use in subsequent enrollment, identification, or authentication, responsive to a determination that the unknown identification sample fails the evaluation criteria; and
as part of validation or rejection, execute at least an evaluation of the unknown identification sample that includes generation of an output probability by the first pre-trained validation helper network that the unknown identification sample is valid or invalid;
wherein the plurality of pre-trained validation helper networks are associated with respective identification information types and the plurality of validation helper networks are trained to generate an evaluation of an unknown identification sample of the respective identification information type and output a probability the respective unknown identification samples is valid or invalid.
2. The system of claim 1 , wherein the first pre-trained validation helper network is configured to identify bad information samples, wherein bad information samples reduce identification accuracy of the subsequent neural networks.
3. The system of claim 1 , wherein the first pre-trained validation helper network is configured to identify good information samples, wherein good identification samples improve identification accuracy of the subsequent neural networks.
4. The system of claim 1 , wherein the first pre-trained validation helper network is configured to identify spoofed information samples.
5. The system of claim 4 , wherein the spoofed information samples include improper submission of valid identification information or indirect capture of valid authentication information.
6. The system of claim 1 , wherein the first pre-trained validation helper network is configured to:
process a video or image input as identification information; and
output a probability that the video or image input is invalid.
7. The system of claim 1 , wherein the first pre-trained validation helper network is configured to:
process a video or image input as identification information, and
output a probability that the video or image input is a presentation attack.
8. The system of claim 1 , wherein the authentication data gateway further comprises a first pre-trained geometry helper network configured to:
process identification information of the first type,
accept as input unencrypted identification information of the first type, and
communicate processed identification information of the first type to the first pre-trained validation helper network.
9. The system of claim 8 , wherein the authentication data gateway further comprises a plurality of pre-trained geometry helper networks configured to:
process identification information of a respective first type,
accept as input unencrypted identification information of the respective type, and
communicate processed identification information of the respective type to an associated pre-trained validation helper network for validating the respective type.
10. A computer implemented method for privacy-enabled authentication, the method comprising:
filtering, by at least one processor, identification information used in enrollment, identification, or authentication functions of subsequent neural networks;
instantiating, by the at least one processor, a first pre-trained validation helper network associated with identification information of a first type, wherein the first pre-trained validation helper network is pre-trained on evaluation criteria that is independent of a subject of the identification information seeking to be enrolled, identified, or authenticated
evaluating, by the first pre-trained validation helper network, an unknown identification sample of the first type, responsive to input of the unknown identification sample of the first type to the first pre-trained validation helper network;
determining, by the first pre-trained validation helper network, if the unknown identification sample meets the evaluation criteria and validating the unknown identification sample for use in subsequent enrollment, identification, or authentication, or if the unknown identification sample fails the evaluation criteria and rejecting the unknown identification sample for use in subsequent enrollment, identification, or authentication; and
generating, by the first pre-trained validation helper network, as part of the acts of validating or rejecting at least an evaluation of the unknown identification sample that includes generation of an output probability by the first pre-trained validation helper network that the unknown identification sample is valid or invalid; and
instantiating a plurality of pre-trained validation helper networks, including the first pre-trained validation helper network, associated with respective identification information types, wherein the plurality of validation helper networks are trained on evaluation criteria independent of a subject of the identification information seeking to be enrolled, identified, or authenticated, and are configured to generate at least an evaluation of an unknown identification sample of the respective identification information type as valid or invalid identification information.
11. The method of claim 10 , wherein the act of determining includes identifying, by the first pre-trained validation helper network, bad information samples, wherein bad information samples reduce identification accuracy of the subsequent neural networks.
12. The method of claim 10 , wherein the act of determining includes identifying, by the first pre-trained validation helper network, good information samples, wherein good identification samples improve identification accuracy of the subsequent neural networks.
13. The method of claim 10 , wherein the method further comprises identifying, by the first pre-trained validation helper network, spoofed information samples.
14. The method of claim 13 , wherein the spoofed information samples include improper submission of valid identification information or indirect capture of valid authentication information.
15. The method of claim 10 , wherein the method further comprises:
processing, by the first pre-trained validation helper network, a video or image input as identification information; and
generating a probability that the video or image input is invalid.
16. The method of claim 10 , wherein the method further comprises:
processing, by the first pre-trained validation helper network, a video or image input as identification information, and
generating a probability that the video or image input is a presentation attack.
17. The method of claim 10 , wherein the method further comprises instantiating, by the at least one processor, a first pre-trained geometry helper network configured to process identification information of the first type, accept as input unencrypted identification information of the first type, and communicate processed identification information of the first type to the first pre-trained validation helper network.
18. The method of claim 17 , wherein the method further comprises instantiating, by the at least one processor, a plurality of pre-trained geometry helper networks configured to process identification information of a respective first type, accept as input unencrypted identification information of the respective type, and communicate processed identification information of the respective type to an associated pre-trained validation helper network for validating the respective type.