Sys-tech Solutions, Inc.

Michael L Soborski

Matthias Voigt

Rafik Ayoub

Prem Swaroop

Bodhayan Dev

Atish P Kamble

Methods, systems, and apparatus, including medium-encoded computer program products, for generating synthetic images, include: obtaining a captured image of a symbol that encodes data; producing a synthetic image from the captured image using a trained machine learning model, wherein the trained machine learning model has been trained using first images and second images of examples of symbols, wherein the second images have a second image quality that is different than a first image quality of first images, and wherein parameters of the machine learning model have been adjusted responsive to image features to tradeoff content loss versus style loss using measurements of information content correlation between third images produced during training and each of the first images and the second images, the measurements being from a comparison metric; and providing the synthetic image for use by a program configured to identify information in images of symbols.

Synthetic image generation using artificial intelligence

In one implementation, a processor: (1) receives an image of a candidate mark from an image acquisition device, (2) uses the image to measure one or more characteristics at a plurality of locations on the candidate mark, resulting in a first set of metrics, (3) removes, from the first set of metrics, a metric having a dominant amplitude, resulting in a trimmed first set of metrics, (4) retrieves, from a computer-readable memory, a second set of metrics that represents one or more characteristics measured at a plurality of locations on an original mark, (5) removes, from the second set of metrics, a metric corresponding to the metric removed from the first set of metrics, resulting in a trimmed second set of metrics, (6) compares the trimmed first set of metrics with the trimmed second set of metrics, and (7) determines whether the candidate mark is genuine based on the comparison.

Methods and a system for verifying the authenticity of a mark using trimmed sets of metrics

A method for determining whether a candidate barcode is genuine involves acquiring an image of an original barcode; determining, from the image of the original barcode, a deviation of a continuous edge of the original barcode from a nominal shape; encoding the deviation as signature data for the original barcode; storing the signature data for the original barcode on a storage device; acquiring an image of the candidate barcode; determining, from the image of the candidate barcode, a deviation of a continuous edge of the candidate barcode from the nominal shape; retrieving the signature data for the original barcode from the storage device; comparing the signature data for the original barcode with the signature data for the candidate barcode; and making a determination that the candidate barcode is genuine or not genuine based on a result of the comparison.

Method and system for determining whether a barcode is genuine using a deviation from a nominal shape

An anti-counterfeiting method involves dividing a barcode image into a plurality of modules; extracting a respective inertia of a gray level co-occurrence matrix for each of the plurality of modules; acquiring an image of a printed candidate bar code; generating a sorted list (using extracted intertias) for the plurality of modules of the image of the printed candidate barcode; in a first range of magnitudes, comparing the sorted list for the image of the printed genuine barcode with an equivalent sorted list for the image of the printed candidate barcode; and in a second range of magnitudes, comparing the sorted list for the image of the printed genuine barcode with the sorted list for the image of the printed candidate barcode, wherein the second range is different from the first range.

Method and system for determining whether a barcode is genuine using a gray level co-occurrence matrix

A method for determining whether a candidate barcode is genuine involves acquiring an image of an original barcode, wherein the original barcode includes a plurality of modules; determining, from the image of the original barcode, a deviation of a position of at least one of the plurality of modules of the original barcode from an idealized grid; encoding the deviation as signature data for the original barcode; storing the signature data for the original barcode on a storage device; acquiring an image of the candidate barcode, wherein the candidate barcode includes a plurality of modules; determining, from the image of the candidate barcode, a deviation of a position of at least one of the plurality of modules of the candidate barcode from the idealized grid; retrieving the signature data for the original barcode from the storage device; comparing the signature data for the original barcode with signature data for the candidate barcode; and making a determination that the candidate barcode is genuine or not genuine based on a result of the comparison.

Method and system for determining whether a barcode is genuine using a deviation from an idealized grid

An anti-counterfeiting method involves carrying out an image processing operation on an image of the printed genuine barcode including superimposing a best-fit grid on the image and extracting edge linearities of a plurality of modules of the image with respect to the best-fit grid; generating a list of the plurality of modules of the image of the printed genuine barcode (sorted based at least in part on the magnitudes of their respective extracted linearities); carrying out the image processing operation on an image of a printed candidate barcode; generating a list for the plurality of modules of the image of the printed candidate barcode (sorted based at least in part on the magnitudes of their respective extracted linearities); in first and second ranges of magnitudes, comparing the sorted list for the image of the printed genuine barcode with the sorted list for the image of the printed candidate barcode.

Method and system for determining an authenticity of a barcode using edge linearity

Methods and a system for verifying the authenticity of a mark

A method of verifying an identity of a printed item includes capturing an image of the unverified printed item, wherein the unverified printed item includes artifacts and at least some of the artifacts were not controllably producible in producing the unverified printed item; extracting information associated with the artifacts of the unverified printed item; ranking the information associated with the artifacts of the unverified printed item; retrieving stored ranked information associated with artifacts of an original printed item from a storage device; and in each of a first and second range of magnitudes, comparing the ranked information associated with the artifacts of the unverified printed item with the ranked information associated with the artifacts of the original printed item.

Methods and system for verifying an authenticity of a printed item

A system for verifying the authenticity of a printed mark includes an image acquisition device that acquires an image of a printed mark. In one implementation, the printed mark includes an identifier that identifies the group or class of an item to which the printed mark is to be (or has been) attached. The system may also include one or more processors that carry out actions such as receiving the image from the image acquisition device; analyzing the image to identify artifacts in the printed mark, retrieving a genuine mark signature from a data storage containing genuine mark signatures, comparing the identified artifacts with the genuine mark signature, and determining whether the unverified item is authentic based on the comparison.

Method and system for determining whether a mark is genuine

According to various implementations, a computing device receives an image of a candidate mark and uses the image to create a profile of a feature of the mark. The computing device filters out, from the profile, all spatial frequency components except for a first band of spatial frequency components, resulting in a first filtered profile for the feature. The computing device repeats this filtering process for a second band of spatial frequency components, resulting in a second filtered profile for the feature, and may repeat this filtering process for further spatial frequency bands. The computing device compares the first filtered profile of the candidate mark with an equivalent first filtered profile of a genuine mark, and may repeat this process for further filtered profiles.

Methods and a computing device for determining whether a mark is genuine

Methods for determining whether a mark is genuine are described. According to various implementations, a computing device (or logic circuitry thereof) receives (e.g., via a camera or via a communication network) an image of a candidate mark (e.g., a one-dimensional or two-dimensional barcode), uses the image to make measurements of a characteristic of a feature of the candidate mark, resulting in a profile for that feature. The computing device filters out, from the feature profile, all spatial frequency components that are indicated to be sibling frequency components. In some embodiments, the computing device carries out the reverse procedure, and filters out all spatial frequency components except for those indicated to be sibling frequency components.

The present disclosure is generally directed to a method and computing device for determining whether a mark is genuine. According to various implementations, a computing device (or logic circuitry thereof) uses unintentionally-produced artifacts within a genuine mark to define an identifiable electronic signature, extracts certain attributes of the signature (such as deviation from the mean value for each band of the signature), and assigns numerical values to the extracted attributes in order to create a hash identifier that is significantly smaller than the electronic signature itself. The hash identifier is then used as an index for a database of electronic signatures (of genuine marks) to enhance the ease and speed with which numerous genuine signatures can be searched (e.g., in a database) and compared with signatures (of candidate marks.

The present disclosure is generally directed to a method and computing device for determining whether a mark is genuine. According to various embodiments, a computing device (or logic circuitry thereof) uses unintentionally-produced artifacts within a genuine mark to define an identifiable electronic signature, and extracts certain location identifiers corresponding to certain measured features of the signature in order to enhance the ease and speed with which numerous genuine signatures can be searched and compared with signatures of candidate marks.

Utilizing natural variations in marked features on an item as a way of establishing an identifier specific to that item, storing the identifier separately from the item, and subsequently accessing the identifier to validate the identity of the item.

Unique identification information from marked features

An illuminator system is provided that is responsive to a trigger signal from a remote source to supply controlled pulses to a load, such as an array of light emitting diodes in a LED strobe. The LED strobe is positioned in close proximity to a lens mounting system. The system of the present invention includes a remote trigger source, a load such as an array of light emitting diodes, and a pulse gate section. The pulse gate section includes a trigger interface having an input connected to the trigger source and is configured to generate a pulse trigger signal in response to a trigger signal from the trigger source. A clock generator responds to the pulse trigger signal by generating at least one clock signal, and a flash pulse generator responds to the clock signal and to at least one user definable input signal by generating a flash pulse signal. The flash pulse signal is used to activate or deactivate the load, and a load drive section is used to drive the load.

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Allentown, NJ, United States of America

Michael L Soborski