How can One-Shot Learning be used to detect signature forgery with minimal genuine signature samples for training

Question

Can you explain with the help of code that How can One-Shot Learning be used to detect signature forgery with minimal genuine signature samples for training?

Answer 1

You can use One-Shot Learning for signature forgery detection by training a Siamese Network that learns to compare genuine and test signatures using similarity scores.

Here is the code snippet you can refer to:

In the above code we are using the following key strategies:

• Uses Siamese architecture for learning signature similarity.

• Requires only one genuine sample per person for inference.

• Leverages pairwise distance to distinguish forgeries.

Hence, One-Shot Learning enables signature forgery detection with minimal data by learning a generalized similarity function instead of user-specific classifiers.