What it is
A generative adversarial network (GAN) is a subclass of machine learning frameworks in which a pair of neural networks, a generator and a discriminator, compete to perform a task, resulting in improved task performance.
For instance, a GAN trained on photographs can generate new photographs that appear to human observers to be at least superficially authentic, exhibiting a variety of realistic characteristics.
When to use it
When sufficient data is not available to train an algorithmic model, GANs can generate new, synthetic data that is representative of the actual data. Additionally, they can identify new cyberattack and malware attack vectors, as well as fraudulent credit-card transactions.
Because the generator is constantly attempting to create new representations of the real data with slight variations, it may generate previously unknown types of possible attacks or fraudulent transactions.
How it works
The generator creates a synthetic representation of the underlying data (e.g., a duplicate image of a person) that is sufficiently accurate to increase the discriminator's error rate. The discriminator attempts to distinguish between genuine and synthetic data.
At first, the generator generates random patterns that the discriminator can easily distinguish from "real" data, but with each attempt, the generator generates more representative data. Over time, the generator will become extremely good at generating synthetic data, thereby increasing the discriminator's error rate.
QE use cases
- Test Data Generation:
When given a set of test data, GANs can generate symmetric test data for a variety of data types, which is advantageous for performing additional functional testing on the System Under Test.
- System Security Testing:
GANs can generate look-alike inputs to test the security of a facial or biometric authentication system when provided with authorized credentials.
- Identify Anomalies in Lab Results:
GANs can be used in healthcare to identify physical anomalies in lab results that could lead to a quicker diagnosis and treatment options for patients.