Overview: TequilaGAN - Identifying GAN Samples

In this chapter, we will learn how to implement TequilaGAN, understand the underlying characteristics of GAN data, and identify data to differentiate real data from fake data. We will also implement strategies to easily identify fake samples generated with the GAN framework.

One strategy is based on the statistical analysis and comparison of raw pixel values and features extracted from them. The other strategy learns formal specifications from the real data and shows that fake samples violate the specifications of the real data.

Topics covered in this chapter

The following topics will be covered in this chapter:

• Identifying GAN samples

• Feature extraction

• Metrics

• Experiments