Audio feature extraction for deepfake audio detection

A method of training a deepfake detection classifier network, comprising the steps of:

• obtaining a bonafide audio resource;
• obtaining a deepfake audio resource;
• determining bonafide audio feature data based on the bonafide audio resource, wherein the bonafide audio feature data includes one or more feature values for each of a plurality of predetermined audio features;
• determining deepfake audio feature data based on the deepfake audio resource, wherein the deepfake audio feature data includes one or more feature values for each of the plurality of predetermined audio features;
• performing a first learning operation for a machine learning (ML) classifier using the bonafide audio feature data;
• and
• performing a second learning operation for the ML classifier using the deepfake audio resource.

A method of detecting a deepfake audio resource, comprising the steps of:

• determining stable audio feature data for a subject audio resource, wherein the stable audio feature data includes feature values for a plurality of predetermined audio features configured to remain invariant under predefined audio transformations;
• inputting the stable audio feature data into a trained one-class classifier configured to model a distribution of bonafide speech of a subject speaker;
• and
• generating a deepfake audio indicator when the stable audio feature data falls outside a learned boundary of the distribution.