Aesthetics-Aware Counterfeit Detection Through Logo Feature Analysis
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Solution Overview
Problem
Conventional e-commerce systems struggle to accurately and efficiently identify counterfeit products due to their reliance on inaccurate template matching and text analysis, leading to inefficiencies in computational and communication resources.
Innovation Solution
A counterfeit identification system that extracts and compares authentic graphic features, including shape, color, and text features, using machine learning models to determine whether a digital image portrays a counterfeit product.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If template matching and text analysis are used to identify counterfeit products, then the detection process can be automated, but the accuracy of identification deteriorates
Solution Approach 1:
The patent segments the logo into multiple regions (e.g., inner circle, outer ring, text areas) and analyzes each region separately using region proposal networks. This segmentation allows the system to focus computational resources on critical authentication features while maintaining high accuracy. Each region is evaluated independently for shape, color, and text properties, then combined to make the final authentication decision.
Solution Approach 2:
The patent applies local quality analysis by examining specific regions of the logo with different analytical approaches. Critical regions like the inner circle and text areas receive more intensive analysis with multiple feature extractions (shape, color, texture), while less critical regions receive standard analysis. This localized approach improves accuracy without uniformly increasing computational burden across the entire image.
2Use of energy by moving object
If conventional template matching is used to detect counterfeit products, then computational resources are consumed, but the efficiency deteriorates due to analyzing entire images
Solution Approach 1:
The patent extracts only the logo region from the product image using object detection algorithms, discarding the rest of the image data. This extraction step reduces the analysis scope from the entire product image to just the critical logo portion, significantly decreasing computational resource consumption while maintaining detection efficiency. The system then focuses all subsequent analysis on this extracted logo region.
Solution Approach 2:
The patent applies partial action by performing comprehensive analysis (multiple feature extractions, region proposals, comparisons) only on the extracted logo region rather than the entire image. This selective intensive analysis of the critical portion achieves high accuracy without the computational cost of analyzing the complete image, effectively balancing resource consumption and productivity.
3Measurement precision
If comprehensive feature extraction is performed on entire digital images, then identification accuracy improves, but computational resource consumption increases
Solution Approach 1:
The patent performs preliminary action by first extracting the logo region and pre-identifying critical regions (inner circle, outer ring, text) before conducting comprehensive feature extraction. This preliminary segmentation and region identification prepares the data structure for efficient subsequent analysis, allowing comprehensive feature extraction to be applied only to relevant regions rather than the entire image, thus maintaining accuracy while reducing computational burden.
Data Source
AI summary
Methods, systems, and non-transitory computer readable media are disclosed for accurately and efficiently identifying digital images that portray counterfeit products. The disclosed system can store key values and properties of authentic graphic elements (logos, trademarks, designs, etc.,). For example, the disclosed system can determine authentic graphic features comprising authentic shape features, authentic color features, and authentic text features corresponding with a graphic element. The disclosed system can further extract a graphic element from a digital image for comparison with the authentic graphic element. For instance, the disclosed system can extract graphic features of the graphic element, the graphic features comprising shape features, color features, and text features from the graphic element. The disclosed system can further determine whether the digital image portrays a counterfeit or an authentic product based on comparing the authentic graphic features with the extracted graphic features.