Intelligent-texture anti-counterfeiting method based on perceptual hashing

Abstract

The invention relates to an intelligent-texture anti-counterfeiting method based on perceptual hashing. A first step is characterized in that image characteristic extraction is performed, which comprises that (1) a perceptual hashing algorithm is used to process an image so as to obtain one visual characteristic vector V (j) of an original texture image; (2) a user uses a mobile phone to scan the texture image to be tested and upload to a server, the perceptual hashing algorithm is used to process the image to be tested and the visual characteristic vector V ' (j) of the image to be tested is acquired. A second step is characterized in that image discrimination is performed, which comprises (3) a normalized correlation coefficient NC value between the visual characteristic vector V (j) of the original texture image and the visual characteristic vector V ' (j) of the image to be tested is acquired; (4) the obtained NC value is returned to the mobile phone of the user. An experiment proves that the method of the invention possesses a strong conventional attack resistance capability and a geometric attack resistance capability. A problem of automatically discriminating the texture image is solved. An intelligent texture anti-counterfeiting technology is realized. Discrimination accuracy is high and a speed is fast.

Description

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AI Technical Summary

Benefits of technology

This patented technique allows creators to verify their own quality by analyzing how well they are doing something while keeping them from being copied or counterfeited without permission. It uses advanced techniques like machine learning algorithms that recognize patterns within data rather than just looking at its content itself. Overall this makes sure things work correctly even if someday there's another way around trying to deceive people into giving away these fake items.

Problems solved by technology

This patented describes different techniques that aimed at identifying genuine items like fake documents (fals). However, these existing technologies had limitations such as requiring expensive equipment, slow recognition speed due to their complexity, lacking accuracy when comparing features from an opaque background, difficulty in distinguishing between legitimate ones, etc., making it challenges to achieve fast and effective automated detection systems.

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