System and method for performing facial image anonymization

A method of performing facial image anonymization, the method comprising:

• obtaining, by a processor of a first computing device, a first digital image of a face;
• encoding, by the processor, the first digital image into a first latent vector;
• modifying, by the processor, the first latent vector, using at least one second NN, trained to receive the first latent vector and to produce therefrom an anonymized latent vector;
• and
• transmitting, by the processor, the anonymized latent vector to a second computing device,
• wherein the second computing device is adapted to decode the anonymized latent vector into a second digital image, depicting an anonymized face,
• wherein training the at least one second NN comprises:
• introducing the first digital image as a first input to a face-recognition (FR) module;
• introducing the second digital image element as a second input to the FR module;
• introducing the first latent vector to at least one feature classifier as a first input;
• introducing the anonymized latent vector to the at least one feature classifier as a second input;
• and
• using output of the FR module and output of the at least one feature classifier as supervisory data, such that the first digital image and second digital image comprise the same features according to the at least one feature classifier, and do not pertain to the same identity according to the FR module.

A system for performing anonymization of a facial image, the system comprising:

• a non-transitory memory device, wherein modules of instruction code are stored, and a processor associated with the memory device, and configured to execute the modules of instruction code, whereupon execution of said modules of instruction code, the processor is further configured to:
• obtain a first image data element, depicting a first face;
• use a first neural network (NN) to encode the first image data element into a first latent vector;
• modify the first latent vector, using at least one second NN, trained to receive the first latent vector and to produce therefrom an anonymized latent vector;
• and
• transmit the anonymized latent vector via a communication network to a second, remote computing device, adapted to decode the anonymized latent vector into a second image data element, depicting an anonymized face,
• wherein training the at least one second NN comprises:
• introducing the first digital image as a first input to a face-recognition (FR) module;
• introducing the second digital image element as a second input to the FR module;
• introducing the first latent vector to at least one feature classifier as a first input;
• introducing the anonymized latent vector to the at least one feature classifier as a second input;
• and
• using output of the FR module and output of the at least one feature classifier as supervisory data, such that the first digital image and second digital image comprise the same features according to the at least one feature classifier, and do not pertain to the same identity according to the FR module.