Object Fingerprinting Authentication With Siamese Prefiltering
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Solution Overview
Problem
Existing authentication methods for luxury items like jewelry and collectibles are inefficient and resource-intensive, particularly due to the lack of effective image classification processes, leading to hesitancy among sellers in accepting returns without ensuring the authenticity of the item.
Innovation Solution
A deep learning-based authentication system using a convolutional Siamese neural network (SNN) with prefiltering analysis to quickly identify obvious unauthentic items, followed by color, texture, and shape matching engines, and then deep learning for final verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning analysis is applied to all items for authentication, then authentication accuracy is improved, but computational resources and processing time are significantly increased
Solution Approach 1:
The system performs preliminary visual feature extraction and basic authentication checks before applying deep learning analysis. This prefiltering step identifies obvious fakes using simpler methods, reserving deep learning computational resources only for items that pass the preliminary screening, thus reducing overall computational burden while maintaining accuracy.
Solution Approach 2:
The authentication process is segmented into multiple stages: preliminary visual feature extraction, basic authentication checks, and deep learning analysis. This segmentation allows the system to apply different levels of computational intensity to different parts of the authentication process, optimizing resource utilization.
2Reliability
If deep learning analysis is performed on all items, then authentication reliability is improved, but processing time and productivity are reduced
Solution Approach 1:
The system performs preliminary visual feature extraction and basic authentication checks before applying deep learning analysis. This prefiltering step quickly identifies obvious fakes using simpler methods, reducing the number of items that require time-consuming deep learning analysis and thus improving overall processing speed while maintaining reliability.
Solution Approach 2:
The authentication process is divided into fast preliminary checks and more thorough deep learning analysis stages. This segmentation allows the system to process items quickly through basic checks and only apply computationally intensive deep learning to items that require it, optimizing the balance between reliability and productivity.
3Productivity
If prefiltering analysis is implemented to filter out obvious fakes, then computational efficiency is improved, but authentication complexity increases
Solution Approach 1:
The authentication system is segmented into multiple specialized modules: visual feature extraction module, prefiltering analysis module, and deep learning analysis module. Each module handles specific tasks, making the overall complex system more manageable and easier to implement through modular architecture.
Solution Approach 2:
The prefiltering analysis acts as an intermediary layer between image input and deep learning analysis. It processes images through visual feature extraction and basic authentication checks before passing suitable candidates to deep learning, simplifying the overall workflow and reducing direct complexity between the most computationally intensive components.
Data Source
AI summary
Systems and methods are directed to authenticating items using object fingerprinting and deep learning. A system extracts visual features from an image of the item to be authenticated. Using the visual features, the system performs prefiltering by comparing the visual features from the image of the item to visual features of an image of an authentic item that is same or similar to the item. Based on the prefiltering analysis indicating that the visual features from the image of the item matches the visual features from the image of the authentic item, deep learning analysis is performed by applying the image of the item and at least two other images including the image of the authentic item to a convolutional Siamese neural network (SNN). The convolutional SNN generates image embeddings used to derive similarity scores. Based on the similarity scores, a determination is made whether the item is authentic.