Method of determining authenticity of a consumer good

1. A method for classifying whether a subject consumer good is authentic or non-authentic comprising the steps:

a) obtaining an image of the subject consumer good comprising a subject product specification;

b) inputting the obtained image into a model;

wherein the model is configured to classify the obtained image as authentic or non-authentic using at least one alphanumerical steganographic feature that has one or more alphanumeric characters modified from customary;

wherein the model is constructed by a machine learning classifier;

wherein the machine learning classifier is trained by a training dataset;

wherein the training dataset comprises: (i) at least one extracted image of an authentic product comprising an authentic product specification comprising at least one alphanumerical steganographic feature having a length greater than 0.01 mm; and (ii) an associated class definition based on said alphanumerical steganographic feature of said authentic product specification of said authentic product; and

c) outputting output from the model classifying the inputted image of the subject consumer good as authentic or non-authentic.

2. The method of claim 1 , wherein the alphanumerical steganographic features has a length from 0.02 mm to 20 mm.

3. The method of claim 1 wherein the alphanumerical steganographic features has a length from 0.03 mm to 5 mm.

4. The method of claim 1 , wherein the machine learning classifier is validated by a validating dataset, wherein the validating dataset comprises identification of the alphanumerical steganographic feature(s) of the authentic product specification generated by alphanumerical steganographic feature generating software.

5. The method of claim 1 , wherein the training dataset comprises annotations annotating the alphanumerical steganographic feature(s).

6. The method of claim 1 , wherein the alphanumerical steganographic feature is selected from: isolated font style for a letter or number or combination thereof; isolated location change of location of text, letter, punctuation, or combination thereof; and combinations thereof.

7. The method of any claim 1 , wherein the alphanumerical steganographic features is automatically generated by way of software.

8. The method of claim 1 , wherein the product specification is selected from the group consisting of production code, batch code, brand name, product line, label, artwork, ingredient list, usage instructions, and combinations thereof.

9. The method of claim 1 , wherein the machine learning classifier is a convolutional neural network.

10. The method of claim 1 , wherein the training dataset is spatially manipulated before training the machine learning classifier.

11. The method of claim 10 , wherein the training dataset is spatially manipulated by way of a spatial transformer network.

12. The method of claim 11 , wherein the training dataset emphasizes high frequency features.

13. The method of claim 1 , wherein the obtained image of the subject consumer good is spatially manipulated before being inputted into the model.

14. The method of claim 13 , wherein the obtained image is spatially manipulated by way of a spatial transformer network.

15. The method of claim 1 , wherein obtaining an image of the subject consumer good is by way of a smart phone.

16. The method of claim 1 , wherein the model resides on a cloud-based server.

17. A method for classifying whether a subject consumer good is authentic or non-authentic comprising the steps:

a) obtaining an image of the subject consumer good comprising a subject product specification;

b) inputting the obtained image into a model;

wherein the model is configured to classify the obtained image as authentic or non-authentic using at least one alphanumerical steganographic feature that has one or more alphanumeric characters modified from customary;

wherein the model is constructed by a machine learning classifier;

wherein the machine learning classifier is trained by a training dataset;

wherein the training dataset comprises: (i) at least one extracted image of an authentic product comprising an authentic product specification comprising alphanumerical Manufacturing Line Variable Printing Code; and (ii) an associated class definition based on said alphanumerical Manufacturing Line Variable Printing Code of said authentic product specification of said authentic product; and

c) outputting output from the model classifying the inputted image of the subject consumer good as authentic or non-authentic.

18. The method of claim 17 , wherein the alphanumerical Manufacturing Line Variable Printing Code is printed by a printing method selected from: continuous ink-jet; embossing, laser etching, thermal transferring, hot waxing, and combinations thereof.