Detection of simulated images using machine learning

A computer system, comprising:

• a processor set;
• one or more computer-readable storage media;
• and
• program instructions stored on the one or more computer-readable storage media, the program instructions executable by the processor set to cause the processor set to:
• generate summary data for an input image;
• generate first result data based on the summary data, wherein the first result data comprises a first set of reasons associated with validation of a representational accuracy of the input image;
• apply a first machine learning (ML) model on the summary data and the input image;
• generate a set of images based on the application of the first ML model on the summary data and the input image;
• generate second result data based on an average similarity score associated with a comparison of the input image with each image in the generated set of images;
• analyze the input image based on a set of quality indicators associated with a set of features of the input image;
• generate a set of ratings for the set of quality indicators, wherein each rating of the set of ratings is indicative of a presence of a corresponding quality indicator in the input image;
• generate third result data based on the set of ratings;
• combine the first result data, the second result data and the third result data to generate final result data;
• and
• output the final result data.

A computer-program product for classifying an input image as a real image or a simulated image, the computer-program product comprising:

• one or more computer-readable storage media;
• and
• program instructions stored on the one or more computer-readable storage media to perform operations comprising:
• generating summary data for the input image;
• generating first result data based on the summary data, wherein the first result data comprises a first set of reasons associated with validation of a representational accuracy of the input image;
• applying a first machine learning (ML) model on the summary data and the input image;
• generating a set of images based on the application of the first ML model on the summary data and the input image;
• generating second result data based on an average similarity score associated with a comparison of the input image with each image in the generated set of images;
• combining the first result data and the second result data to generate final result data;
• classifying the input image as one of the real image or the simulated image based on the final result data;
• and
• output the classified input image.