Deep neural network based identification of realistic synthetic images generated using a generative adversarial network

A method, comprising:

• training, by a system operatively coupled to a processor, a deep neural network model to classify synthetic images generated using a synthetic image generation model as either real-like or non-real like using a training dataset comprising first real images and first synthetic images generated using the synthetic image generation model and classified as non-real like, resulting in a trained deep neural network model;
• classifying, by the system, second synthetic images generated using the synthetic image generation model as either real-like or non-real-like using the trained deep neural network model;
• and
• employing, by the system, a subset of the second synthetic images classified as real-like to train a machine learning model to perform artificial intelligence analytics on real images.

A machine-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:

• training, by a system operatively coupled to a processor, a deep neural network model to classify synthetic images generated using a synthetic image generation model as either real-like or non-real like using a training dataset comprising first real images and first synthetic images generated using the synthetic image generation model and classified as non-real like, resulting in a trained deep neural network model;
• classifying second synthetic images generated using the synthetic image generation model as either real-like or non-real-like using the trained deep neural network model;
• and
• employing a subset of the second synthetic images classified as real-like to train a machine learning model to perform artificial intelligence analytics on real images.