Detecting synthetic visual media

A method for training a machine learning (ML) model to detect synthetic visual media, the method comprising the steps of:

• providing a plurality of genuine visual media;
• providing a plurality of synthetic visual media;
• for each visual media in the plurality of synthetic visual media and genuine visual media:
• identifying at least one feature within the visual media;
• identifying a first portion of the visual media and second portion of the visual media, the first portion of the visual media containing the at least one feature and the second portion of the visual media not including the at least one feature;
• providing to an ML model the first and second portions of the visual media;
• obtaining data from the ML model indicating that the visual media is a synthetic and/or that the visual media is genuine;
• and
• based on the data obtained from the ML model and data stating that the visual media is synthetic or genuine, updating the ML model,
• wherein for each visual media in the plurality of synthetic visual media and genuine visual media before being provided to the ML model transforming the first portion and the second portion of the visual media into the frequency domain to form embeddings.

A non-transitory computer-readable medium storing instructions that, when read by an apparatus, cause the apparatus to:

• on receipt of visual media, wherein the visual media is synthetic visual media or genuine visual media;
• identifying at least one feature within the visual media;
• identifying a first portion of the visual media and second portion of the visual media, the first portion of the visual media containing the at least one feature and the second portion of the visual media not including the at least one feature;
• generating embeddings for the first portion of the visual media and the second portion of the visual media, wherein the step of generating embeddings further comprises the step of converting the first portion of the visual media and the second portion of the visual media into the frequency domain;
• providing the first portion and the second portion to a trained machine learning (ML) model;
• and
• the trained ML model providing data indicating the visual media to be synthetic and/or genuine.