System and method for image de-identification to humans while remaining recognizable by machines

A method of generating a neural network (NN) model, the method comprising:

• training a NN including the model by, for each input image in a set of input images of faces:
• generating, using the model, an output image of the face;
• calculating, by a first function, a dis-similarity score reflecting the likelihood that a human would associate the face in the input image with the face in the output image;
• calculating, by a second function, an identity-similarity score reflecting an ability of a computerized unit to associate the face in the input image with the face in the output image;
• and
• modifying parameters of the model using a loss function designed to manipulate style features while maintaining structure features such that the dis-similarity score reaches a first threshold indicating that the human would not be able to associate the face in the input image with the face in the output image, and the identity-similarity score reaches a second threshold indicating that the computerized unit would be able to associate the face in the input image with the face in the output image,
• wherein the style features comprise high level features of the model,
• wherein modifying the style features while maintaining the structure features is carried out with application of a loss function :
• =λ0∥Θ0(Xsrc)−Θ0(XD)∥−λ1∥Θ1(Xsrc)−Θ1(XD)∥ . . . −λk∥Θk(Xsrc)−Θk(XD)∥
• where k is a scale index, λ is a weight, Θ is a parameter of the neural network, Xsrc is the input image, XD is the output image, and Θk are the high level features.

A system of image de-identification, the system comprising:

• a memory;
• and
• a processor configured to:
• receive an input image of a human face;
• iteratively modify the input image to produce an output image of the human face by modifying style parameters while maintaining structure features of the input image until:
• a score indicating how unlikely a human is to associate the input image with the output image reaches a first threshold indicating that the human would not be able to associate the input image with the output image, and
• a score indicating an ability of a computerized unit to associate the input image with the output image reaches a second threshold indicating that the computerized unit would be able to associate the input image with the output image;
• and
• providing the output image,
• wherein the processor is further configured to modify the input image by a deep neural network, wherein the style parameters comprise high level features of the neural network, and wherein the neural network modifies the input image by modifying the high level features while maintaining the structure features,
• wherein modifying the style parameters while maintaining the structure features is carried out with application of a loss function :
• =λ0∥Θ0(Xsrc)−Θ0(XD)∥−λ1∥Θ1(Xsrc)−Θ1(XD)∥ . . . −λk∥Θk(Xsrc)−Θk(XD)∥
• where k is a scale index, λ is a weight, Θ is a parameter of the neural network, Xsrc is the input image, XD is the output image, and Θk are the high level features.