US 11,899,774 B2
Method and apparatus for determining authenticity of an information bearing device
Tak Wai Lau, Hong Kong (HK); and Wing Hong Lam, Hong Kong (HK)
Assigned to INFOTOO INTERNATIONAL LIMITED, Hong Kong (HK)
Appl. No. 16/977,100
Filed by INFOTOO INTERNATIONAL LIMITED, Hong Kong (HK)
PCT Filed Mar. 1, 2019, PCT No. PCT/IB2019/051653
§ 371(c)(1), (2) Date Sep. 1, 2020,
PCT Pub. No. WO2019/167007, PCT Pub. Date Sep. 6, 2019.
Claims priority of application No. 18102975.5 (HK), filed on Mar. 1, 2018.
Prior Publication US 2020/0410510 A1, Dec. 31, 2020
Int. Cl. G06K 9/00 (2022.01); G06F 21/44 (2013.01); G06N 3/08 (2023.01); G06Q 30/018 (2023.01); G06T 9/00 (2006.01)
CPC G06F 21/44 (2013.01) [G06N 3/08 (2013.01); G06Q 30/0185 (2013.01); G06T 9/002 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An authentication apparatus configured for determining whether a captured image is a primary image or a non-primary image of an authentic information bearing device, the authentication apparatus comprising a microprocessor, a display, a data storage device, and an authentication tool which is resident in the data storage device;
wherein the authentic information bearing device comprises a data-embedded image pattern which is encoded with a set of discrete data, wherein each discrete data of the set of discrete data has a characteristic signal strength and correlates with a set of spatially distributed pattern defining elements of the data-embedded image pattern by a domain transformation function, the spatially distributed pattern defining elements being spread in the data-embedded image pattern to form a spread image pattern;
wherein the authentication tool comprises a trained neural network having a neural network structure comprising an input layer, an output layer and a plurality of neural network layers intermediate the input layer and the output layer, and each neural network layer has a set of filters comprising a plurality of learned filters; wherein each learned filter comprises a plurality of filter elements and each filter element has a filter value, and wherein the learned filters and filter values were composed or constituted by computer based-deep-learning using a plurality of training images;
wherein the training images comprises a plurality of primary images of the data-embedded image pattern and a plurality of non-primary images of the data-embedded image pattern of the data-embedded image pattern, and wherein a primary image and a corresponding non-primary image have different characteristic signal strengths; and
wherein the trained neural network was trained to determine whether a captured image is a primary image or a non-primary image with reference to the characteristic signal strengths of the training images.