Packaging Authentication With ML-Guided Image Capture Feedback
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Solution Overview
Problem
Counterfeit products are often packaged in packaging that mimics authentic products, making it difficult to authenticate the authenticity of the product based on packaging analysis.
Innovation Solution
A method using machine learning models to identify packaging faces in images, determine capture conditions, and provide feedback for image capture, followed by authentication using digital blueprints generated from reference images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If packaging analysis is performed to authenticate products, then authentication accuracy can be improved, but computational complexity and resource usage increase
Solution Approach 1:
The authentication process is divided into distinct stages: image capture with guidance, face detection and classification, capture condition evaluation, and authentication comparison. Each stage is handled by specialized machine learning models that process only relevant aspects of the packaging, reducing overall computational complexity while maintaining authentication accuracy.
Solution Approach 2:
The system performs preliminary actions by providing real-time feedback during image capture to guide users in obtaining optimal images before authentication begins. Capture conditions are evaluated upfront, and the system selects appropriate reference images in advance, preventing unnecessary computational processing of poor-quality images and reducing overall resource usage.
2Measurement precision
If real-time feedback is provided during image capture, then user guidance and authentication accuracy improve, but processing time and computational resources increase
Solution Approach 1:
The system evaluates capture conditions periodically during the image capture process rather than continuously analyzing every pixel change. Machine learning models assess key parameters at strategic intervals, providing timely feedback to users without requiring constant heavy processing, thus balancing real-time guidance with efficient resource utilization.
3Reliability
If multiple machine learning models are used to evaluate capture conditions and identify packaging faces, then authentication reliability improves, but device complexity and resource consumption increase
Solution Approach 1:
Different machine learning models are assigned to specific tasks: one model identifies packaging faces, another evaluates capture conditions, and a third performs authentication comparison. This segmentation allows each model to be optimized for its specific function, improving overall reliability while managing system complexity through modular architecture.
Solution Approach 2:
The machine learning models are designed to perform multiple functions where possible. For example, the face detection model also provides information useful for capture condition evaluation, and reference image selection leverages previously processed data. This multi-functionality reduces the need for separate specialized models, managing complexity while maintaining reliability.
4Productivity
If computational resources are optimized to reduce usage, then processing speed and efficiency improve, but authentication accuracy may deteriorate
Solution Approach 1:
The system extracts and processes only the most critical features for authentication: packaging face identification, capture condition compliance, and key visual characteristics. By extracting only essential information rather than processing complete high-resolution images through all analysis stages, the system maintains authentication accuracy while significantly reducing computational resource requirements.
Solution Approach 2:
The system performs partial processing on images that fail initial capture condition checks, focusing computational resources only on potentially authentic images that meet minimum quality thresholds. This selective processing approach ensures adequate authentication accuracy for viable candidates while avoiding wasteful computation on clearly insufficient images.
Data Source
AI summary
A method includes capturing an image using a camera of a mobile device; processing the image using one or more machine learning models, wherein the one or more machine learning models have been trained to identify a face of first packaging in the image, and determine whether the first packaging in the image satisfies one or more capture conditions; providing feedback for image capture based on a first output of the one or more machine learning models relating to the one or more capture conditions; and in response to output of the one or more machine learning models indicating that the one or more capture conditions are satisfied, and in response to the output of the one or more machine learning models indicating that the face of the first packaging is present in the image, sending the image for authentication of the first packaging.