According to a study conducted by the University of Surrey, the integration of synthetic data can significantly enhance the precision of identity document recognition systems, with an improvement of up to 20%. Researchers utilised a blend of synthetic and real-world data to train their system, and they observed that the inclusion of synthetic data led to a reduction in errors caused by factors such as image distortion, poor lighting, and document variations.
This points to the idea that the use of synthetic data is transforming the creation and usage of synthetic ID documents through the influence of technology, but how is this being done?
Training identity document recognition systems
Synthetic data has proven to be a useful tool in training identity document recognition systems, especially where the collection of large, diverse, and accurate training data is challenging or impossible. In this approach, a generative model is used to create synthetic images of identity documents that mimic real-world examples. The synthetic data is generated by simulating a range of image distortions, variations in lighting conditions, and other visual factors that are commonly encountered in the real world.
One of the main benefits of using synthetic data for training is that it can be generated quickly and inexpensively, allowing developers to create large volumes of high-quality training data without the need for time-consuming and costly manual data collection. Synthetic data also provides greater control over the training data, allowing developers to create a more diverse and balanced training set that covers a broader range of scenarios and use cases.
Synthetic data can be manipulated to simulate scenarios that are difficult or risky to replicate in the real world, such as the testing of fraud detection algorithms. By creating synthetic identities with realistic but artificial features, developers can test the system’s robustness against a range of potential threats without compromising sensitive information.
Privacy and identity
Using real identity documents for training and testing purposes can pose a significant risk to individuals’ privacy and security, particularly with regards to personally identifiable information (PII). Moreover, the General Data Protection Regulation (GDPR) requires organisations to protect PII and only process it under certain conditions. However, using synthetic data can help to overcome this problem by allowing developers to create realistic-looking documents that contain no PII. This means that developers can test and refine their identity document recognition systems without putting individuals’ personal information at risk, thereby ensuring compliance with GDPR and other data privacy regulations.
In addition to protecting individuals’ privacy and security, synthetic data can also help to improve the accuracy and reliability of identity document recognition systems. By using synthetic data, developers can create large and diverse datasets that contain a wide range of identity documents from different countries and regions. This can help to ensure that the system is robust and can accurately identify documents from a variety of sources, including passports, national IDs, driver’s licences, and other identity documents.
Another advantage of synthetic data is that it can be easily customised to suit specific use cases. For example, developers can create synthetic data that includes specific types of identity documents or that is tailored to a particular demographic group. This can help to ensure that the system performs well in the specific context in which it will be used, thereby improving its overall performance and compliance with relevant regulations.
Overall, synthetic data has the potential to revolutionise the development and testing of identity document recognition systems. By providing a safe and reliable way to create large and diverse datasets, synthetic data can help to improve the accuracy and reliability of these systems while protecting individuals’ privacy and security, and ensuring compliance with relevant data privacy regulations such as GDPR. As such, it is likely that we will see increased innovation and development in this field with the continued use of synthetic data.
Securing Identities with Synthetic Data: How AI is Revolutionising Identity Document Recognition was originally published in MindtechGlobal on Medium, where people are continuing the conversation by highlighting and responding to this story.