Tamper detection for identification documents
Methods for detecting digital or physical tampering of an imaged physical credential include the actions of: receiving a digital image representing a physical credential having one or more high value regions, the digital image including an array of pixels; processing the digital image with a tamper detector to generate an output corresponding to an intrinsic characteristic of the digital image, the tamper detector configured to perform a pixel-level analysis of the high value regions of the digital image with respect to a predetermined tampering signature; and determining, based on the output from the tamper detector, whether the digital image has been digitally tampered with.
1 . A method for detecting a counterfeit credential, the method comprising:
obtaining, using one or more computers, an image depicting a physical credential;
generating, using one or more computers, input data representing the obtained image;
providing, using one or more computers, the generated input data as an input to a machine learning model that has been trained to generate output data indicating a likelihood that a physical credential has been physically tampered with based on processing an image of the physical credential;
processing, using one or more computers, the provided input data through the machine learning model to generate output data indicating a likelihood that a physical credential has been physically tampered with based on processing an image of the physical credential;
determining, using one or more computers, whether the generated output data satisfies a predetermined threshold; and
based on a determination that the generated output data satisfies the predetermined threshold, generating an alert indicating that the physical credential depicted by the obtained image is likely a counterfeit credential.
2 . The method of claim 1 , wherein the machine learning model has been trained on a plurality of training data images, wherein the training data image comprises:
a first plurality of images, wherein each particular image in the first plurality of images have each been labeled with a first label type, the first label type indicating that the particular image in the first plurality of images depicts a physical credential that has been physically tampered with; and
a second plurality of images, wherein each particular image in the second plurality of images have been labeled with a second label type, the second label type indicating that the particular image in the second plurality of images depicts a physical credential that has not been physically tampered with.
3 . The method of claim 2 , wherein a training data image in the first plurality of images comprises an image depicting one or more indications of a physical modification to the physical credential.
4 . The method of claim 1 , wherein the physical modification comprises a manual modification to the physical credential that misrepresents a feature of the physical credential.
5 . The method of claim 4 , wherein the manual modification to the physical credential that misrepresents a feature of the physical credential comprises (i) a sticker or post-manufacturing material affixed to at least a portion of a surface of the physical credential or (ii) a marking on at least a portion of the surface of the physical credential.
6 . The method of claim 5 , wherein the portion of the surface of the physical credential is (i) a portion of the physical credential corresponding to one or more bibliographic fields of the physical credential, (ii) a portion of the physical credential corresponding to a photo of a portion, (iii) a portion of the physical credential corresponding to a machine-readable zone (MRZ), (iv) a portion of the physical credential corresponding to biometric information, or any combination thereof.
7 . The method of claim 1 , wherein the method further comprising:
based on a determination that the generated output data does not satisfy the predetermined threshold, generating output data indicating that the physical credential is likely not a counterfeit credential.
8 . A system for detecting a counterfeit credential, the system comprising:
one or more computers; and
one or more memory devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations, the operations comprising:
obtaining, using the one or more computers, an image depicting a physical credential;
generating, using the one or more computers, input data representing the obtained image;
providing, using the one or more computers, the generated input data as an input to a machine learning model that has been trained to generate output data indicating a likelihood that a physical credential has been physically tampered with based on processing an image of the physical credential;
processing, using the one or more computers, the provided input data through the machine learning model to generate output data indicating a likelihood that a physical credential has been physically tampered with based on processing an image of the physical credential;
determining, using the one or more computers, whether the generated output data satisfies a predetermined threshold; and
based on a determination that the generated output data satisfies the predetermined threshold, generating, using the one or more computers, an alert indicating that the physical credential depicted by the obtained image is likely a counterfeit credential.
9 . The system of claim 8 , wherein the machine learning model has been trained on a plurality of training data images, wherein the training data image comprises:
a first plurality of images, wherein each particular image in the first plurality of images have each been labeled with a first label type, the first label type indicating that the particular image in the first plurality of images depicts a physical credential that has been physically tampered with; and
a second plurality of images, wherein each particular image in the second plurality of images have been labeled with a second label type, the second label type indicating that the particular image in the second plurality of images depicts a physical credential that has not been physically tampered with.
10 . The system of claim 9 , wherein a training data image in the first plurality of images comprises an image depicting one or more indications of a physical modification to the physical credential.
11 . The system of claim 10 , wherein the physical modification comprises a manual modification to the physical credential that misrepresents a feature of the physical credential.
12 . The system of claim 11 , wherein the manual modification to the physical credential that misrepresents a feature of the physical credential comprises (i) a sticker or post-manufacturing material affixed to at least a portion of a surface of the physical credential or (ii) a marking on at least a portion of the surface of the physical credential.
13 . The system of claim 12 , wherein the portion of the surface of the physical credential is (i) a portion of the physical credential corresponding to one or more bibliographic fields of the physical credential, (ii) a portion of the physical credential corresponding to a photo of a portion, (iii) a portion of the physical credential corresponding to a machine-readable zone (MRZ), (iv) a portion of the physical credential corresponding to biometric information, or any combination thereof.
14 . The system of claim 8 , the operations further comprising:
based on a determination that the generated output data does not satisfy the predetermined threshold, generating output data indicating that the physical credential is likely not a counterfeit credential.
15 . One or more non-transitory computer-readable storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations for detecting a counterfeit credential, the operations comprising:
obtaining an image depicting a physical credential;
generating input data representing the obtained image;
providing the generated input data as an input to a machine learning model that has been trained to generate output data indicating a likelihood that a physical credential has been physically tampered with based on processing an image of the physical credential;
processing the provided input data through the machine learning model to generate output data indicating a likelihood that a physical credential has been physically tampered with based on processing an image of the physical credential;
determining whether the generated output data satisfies a predetermined threshold; and
based on a determination that the generated output data satisfies the predetermined threshold, generating an alert indicating that the physical credential depicted by the obtained image is likely a counterfeit credential.
16 . The one or more non-transitory computer-readable storage devices of claim 15 , wherein the machine learning model has been trained on a plurality of training data images, wherein the training data image comprises:
a first plurality of images, wherein each particular image in the first plurality of images have each been labeled with a first label type, the first label type indicating that the particular image in the first plurality of images depicts a physical credential that has been physically tampered with; and
a second plurality of images, wherein each particular image in the second plurality of images have been labeled with a second label type, the second label type indicating that the particular image in the second plurality of images depicts a physical credential that has not been physically tampered with.
17 . The one or more non-transitory computer-readable storage devices of claim 16 , wherein a training data image in the first plurality of images comprises an image depicting one or more indications of a physical modification to the physical credential.
18 . The one or more non-transitory computer-readable storage devices of claim 17 , wherein the physical modification comprises a manual modification to the physical credential that misrepresents a feature of the physical credential.
19 . The one or more non-transitory computer-readable storage devices of claim 18 , wherein the manual modification to the physical credential that misrepresents a feature of the physical credential comprises (i) a sticker or post-manufacturing material affixed to at least a portion of a surface of the physical credential or (ii) a marking on at least a portion of the surface of the physical credential.
20 . The one or more non-transitory computer-readable storage devices of claim 19 , wherein the portion of the surface of the physical credential is (i) a portion of the physical credential corresponding to one or more bibliographic fields of the physical credential, (ii) a portion of the physical credential corresponding to a photo of a portion, (iii) a portion of the physical credential corresponding to a machine-readable zone (MRZ), (iv) a portion of the physical credential corresponding to biometric information, or any combination thereof.
21 . The one or more non-transitory computer-readable storage devices of claim 15 , the operations further comprising:
based on a determination that the generated output data does not satisfy the predetermined threshold, generating output data indicating that the physical credential is likely not a counterfeit credential.