Siamese Network Signature Forgery Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods are inadequate in distinguishing between genuine and forged signatures, particularly skilled forgeries, which require sophisticated tools to detect minute differences.
Innovation Solution
A hierarchical framework using deep convolutional Siamese networks is employed, with a first model trained for blind forgery detection and a second model trained with triplets for skilled forgery detection, utilizing pairwise and triplet loss functions to differentiate between genuine and forged signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sophisticated tools are used to detect skilled forgeries, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the forgery detection task into two distinct models: a first model for blind forgeries and a second model for skilled forgeries. This segmentation allows each model to be optimized for specific detection scenarios, improving overall precision while managing complexity through modular architecture
Solution Approach 2:
The system transitions from traditional single-model approaches to a hierarchical two-dimensional detection framework. The first dimension handles blind forgeries with basic comparison, while the second dimension addresses skilled forgeries with advanced triplet analysis, enabling precise detection across different forgery types without overwhelming system complexity
2Device complexity
If traditional detection methods are used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
Rather than using a single complex system, the invention segments detection into two specialized models with distinct functions. The first model handles straightforward blind forgery cases, while the second model addresses more challenging skilled forgeries, achieving high precision through division of labor
Solution Approach 2:
The system creates multiple copies of neural network models with different training objectives. The first model is trained on blind forgery pairs, while the second model is trained on skilled forgery triplets. These copied models work together to provide comprehensive detection coverage without requiring a single overly complex system
3Device complexity
If a single detection model is used, then device complexity is minimized, but adaptability to different forgery types deteriorates
Solution Approach 1:
The system achieves multi-functionality by deploying two models that work together: the first model provides universal detection for blind forgeries, while the second model extends capability to skilled forgeries. This universal-two-model architecture covers multiple forgery types without requiring separate specialized systems for each
4Measurement precision
If sophisticated detection tools are deployed, then detection precision is improved, but computational resources required increase
Solution Approach 1:
The computational workload is segmented between two models with different resource requirements. The first model handles blind forgeries with lower computational demands, while the second model processes skilled forgeries only when needed, optimizing overall resource utilization while maintaining high detection precision
Solution Approach 2:
The system applies partial action by using the simpler first model for routine blind forgery detection and reserving the more computationally intensive second model for skilled forgery cases. This selective application of computational resources achieves high precision without consistently consuming maximum computational power
Data Source
AI summary
Two models are first trained and then test images are applied to the two trained models in an effort to detect signature forgeries. The first model is trained with pairs of signature images and the resultant trained model is capable of detecting blind forgeries. The second model is trained with triplets of signature images and is capable of detecting skilled signature forgeries. After the two models are trained, test images are applied to the models and determinations are made as to whether a blind or skilled forgery is present.